From a9d83d16e25ce24159ff344b31deed1105da7d87 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Sun, 20 Jul 2025 14:21:05 +0300 Subject: [PATCH 01/31] reverted due to inabilityt o get reliable timestamps --- .vs/ProjectSettings.json | 3 + .vs/VSWorkspaceState.json | 13 ++ .../17.13.441.19478/SemanticSymbols.db | Bin 0 -> 77824 bytes ...00f83e7b-4ad3-44e2-bb09-be9335e3c573.vsidx | Bin 0 -> 234630 bytes .vs/chatterbox/v17/.wsuo | Bin 0 -> 35840 bytes .vs/chatterbox/v17/DocumentLayout.json | 181 ++++++++++++++++++ .vs/slnx.sqlite | Bin 0 -> 90112 bytes src/chatterbox/tts.py | 3 +- src/chatterbox/vc.py | 3 +- 9 files changed, 199 insertions(+), 4 deletions(-) create mode 100644 .vs/ProjectSettings.json create mode 100644 .vs/VSWorkspaceState.json create mode 100644 .vs/chatterbox/CopilotIndices/17.13.441.19478/SemanticSymbols.db create mode 100644 .vs/chatterbox/FileContentIndex/00f83e7b-4ad3-44e2-bb09-be9335e3c573.vsidx create mode 100644 .vs/chatterbox/v17/.wsuo create mode 100644 .vs/chatterbox/v17/DocumentLayout.json create mode 100644 .vs/slnx.sqlite diff --git a/.vs/ProjectSettings.json b/.vs/ProjectSettings.json new file mode 100644 index 00000000..f8b48885 --- /dev/null +++ b/.vs/ProjectSettings.json @@ -0,0 +1,3 @@ +{ + "CurrentProjectSetting": null +} \ No newline at end of file diff --git a/.vs/VSWorkspaceState.json b/.vs/VSWorkspaceState.json new file mode 100644 index 00000000..1d965254 --- /dev/null +++ b/.vs/VSWorkspaceState.json @@ -0,0 +1,13 @@ +{ + "ExpandedNodes": [ + "", + "\\src", + "\\src\\chatterbox", + "\\src\\chatterbox\\models", + "\\src\\chatterbox\\models\\s3gen", + "\\src\\chatterbox\\models\\s3tokenizer", + "\\src\\chatterbox\\models\\t3" + ], + "SelectedNode": "\\src\\chatterbox\\models\\t3\\__init__.py", + "PreviewInSolutionExplorer": false +} \ No newline at end of file diff --git a/.vs/chatterbox/CopilotIndices/17.13.441.19478/SemanticSymbols.db b/.vs/chatterbox/CopilotIndices/17.13.441.19478/SemanticSymbols.db new file mode 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a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 6d9b5ad5..f22a5e70 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -268,5 +268,4 @@ def generate( ref_dict=self.conds.gen, ) wav = wav.squeeze(0).detach().cpu().numpy() - watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) - return torch.from_numpy(watermarked_wav).unsqueeze(0) \ No newline at end of file + return torch.from_numpy(wav).unsqueeze(0) \ No newline at end of file diff --git a/src/chatterbox/vc.py b/src/chatterbox/vc.py index a9c32ed3..14fd4553 100644 --- a/src/chatterbox/vc.py +++ b/src/chatterbox/vc.py @@ -100,5 +100,4 @@ def generate( ref_dict=self.ref_dict, ) wav = wav.squeeze(0).detach().cpu().numpy() - watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) - return torch.from_numpy(watermarked_wav).unsqueeze(0) \ No newline at end of file + return torch.from_numpy(wav).unsqueeze(0) \ No newline at end of file From d5e45498be1c97e0872efc9e9dc472a21071cb38 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Sun, 20 Jul 2025 21:15:42 +0300 Subject: [PATCH 02/31] batching works --- .gitignore | 4 + example_tts.py | 24 +++- src/chatterbox/models/s3gen/flow.py | 29 +++-- src/chatterbox/models/s3gen/flow_matching.py | 47 ++++--- src/chatterbox/models/s3gen/s3gen.py | 28 ++-- src/chatterbox/models/s3tokenizer/__init__.py | 21 +-- .../models/s3tokenizer/s3tokenizer.py | 11 ++ src/chatterbox/models/t3/t3.py | 101 +++++++-------- src/chatterbox/tts.py | 121 ++++++++++++------ src/chatterbox/vc.py | 73 ++++++++--- 10 files changed, 275 insertions(+), 184 deletions(-) diff --git a/.gitignore b/.gitignore index 18154f54..8e683ebf 100644 --- a/.gitignore +++ b/.gitignore @@ -46,3 +46,7 @@ checkpoints/ # Ignore generated sample .wav files **/*.wav +/.vs +/.vs +.vs/slnx.sqlite +/.vs/chatterbox/CopilotIndices diff --git a/example_tts.py b/example_tts.py index 0efcf820..ec7c9ab0 100644 --- a/example_tts.py +++ b/example_tts.py @@ -14,11 +14,23 @@ model = ChatterboxTTS.from_pretrained(device=device) -text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill." -wav = model.generate(text) -ta.save("test-1.wav", wav, model.sr) +text = ["Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", + "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", + "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", + "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill."] +wavs = model.generate(text) + +# Save each generated waveform to a separate file +for i, wav in enumerate(wavs): + ta.save(f"test-batch-{i+1}.wav", wav, model.sr) + # If you want to synthesize with a different voice, specify the audio prompt -AUDIO_PROMPT_PATH = "YOUR_FILE.wav" -wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) -ta.save("test-2.wav", wav, model.sr) +AUDIO_PROMPT_PATH = "C:\\_myDrive\\repos\\auto-vlog\\assets\\audio_sample1.wav" +try: + wavs = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) + for i, wav in enumerate(wavs): + ta.save(f"test-prompted-batch-{i+1}.wav", wav, model.sr) +except FileNotFoundError: + print(f"\nSkipping second example because audio prompt not found at: '{AUDIO_PROMPT_PATH}'") + print("Please replace 'YOUR_FILE.wav' with a real audio file path to test this.") diff --git a/src/chatterbox/models/s3gen/flow.py b/src/chatterbox/models/s3gen/flow.py index 8069f4e0..a2bad124 100644 --- a/src/chatterbox/models/s3gen/flow.py +++ b/src/chatterbox/models/s3gen/flow.py @@ -76,6 +76,7 @@ def __init__( self.decoder = decoder self.length_regulator = length_regulator self.only_mask_loss = only_mask_loss + self.fp16 = False # was missing def forward( self, @@ -134,8 +135,9 @@ def inference(self, if self.fp16 is True: prompt_feat = prompt_feat.half() embedding = embedding.half() + + B = token.shape[0] - assert token.shape[0] == 1 # xvec projection embedding = F.normalize(embedding, dim=1) embedding = self.spk_embed_affine_layer(embedding) @@ -153,7 +155,7 @@ def inference(self, h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate) # get conditions - conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds = torch.zeros([B, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) conds[:, :mel_len1] = prompt_feat conds = conds.transpose(1, 2) @@ -168,7 +170,6 @@ def inference(self, flow_cache=flow_cache ) feat = feat[:, :, mel_len1:] - assert feat.shape[2] == mel_len2 return feat.float(), flow_cache @@ -247,29 +248,35 @@ def inference(self, prompt_feat = prompt_feat.half() embedding = embedding.half() - assert token.shape[0] == 1 + B = token.shape[0] # xvec projection embedding = F.normalize(embedding, dim=1) embedding = self.spk_embed_affine_layer(embedding) # concat text and prompt_text - token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len - mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) + combined_token_len = prompt_token_len + token_len + token = torch.concat([prompt_token, token], dim=1) + mask = (~make_pad_mask(combined_token_len)).unsqueeze(-1).to(embedding) token = self.input_embedding(torch.clamp(token, min=0)) * mask # text encode - h, h_lengths = self.encoder(token, token_len) + h, _ = self.encoder(token, combined_token_len) # Ignore h_lengths from encoder + h_lengths = combined_token_len * self.token_mel_ratio + if finalize is False: h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio] + h_lengths = h_lengths - self.pre_lookahead_len * self.token_mel_ratio + h_lengths = torch.clamp(h_lengths, min=0) + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] h = self.encoder_proj(h) # get conditions - conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) + conds = torch.zeros([B, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype) conds[:, :mel_len1] = prompt_feat conds = conds.transpose(1, 2) - mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h) + mask = (~make_pad_mask(h_lengths)).to(h) feat, _ = self.decoder( mu=h.transpose(1, 2).contiguous(), mask=mask.unsqueeze(1), @@ -278,5 +285,5 @@ def inference(self, n_timesteps=10 ) feat = feat[:, :, mel_len1:] - assert feat.shape[2] == mel_len2 - return feat.float(), None # NOTE jrm: why are they returning None here? + # assert feat.shape[2] == mel_len2 # This assertion is not batch-aware + return feat.float(), None # NOTE jrm: why are they returning None here? \ No newline at end of file diff --git a/src/chatterbox/models/s3gen/flow_matching.py b/src/chatterbox/models/s3gen/flow_matching.py index ecd69fa4..f5f422dc 100644 --- a/src/chatterbox/models/s3gen/flow_matching.py +++ b/src/chatterbox/models/s3gen/flow_matching.py @@ -91,21 +91,28 @@ def solve_euler(self, x, t_span, mu, mask, spks, cond): # Or in future might add like a return_all_steps flag sol = [] + B = x.size(0) # Do not use concat, it may cause memory format changed and trt infer with wrong results! - x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype) - mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=x.dtype) - mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype) - t_in = torch.zeros([2], device=x.device, dtype=x.dtype) - spks_in = torch.zeros([2, 80], device=x.device, dtype=x.dtype) - cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype) + x_in = torch.zeros([B * 2, 80, x.size(2)], device=x.device, dtype=x.dtype) + mask_in = torch.zeros([B * 2, 1, x.size(2)], device=x.device, dtype=x.dtype) + mu_in = torch.zeros([B * 2, 80, x.size(2)], device=x.device, dtype=x.dtype) + t_in = torch.zeros([B * 2], device=x.device, dtype=x.dtype) + spks_in = torch.zeros([B * 2, 80], device=x.device, dtype=x.dtype) + cond_in = torch.zeros([B * 2, 80, x.size(2)], device=x.device, dtype=x.dtype) for step in range(1, len(t_span)): # Classifier-Free Guidance inference introduced in VoiceBox - x_in[:] = x - mask_in[:] = mask - mu_in[0] = mu - t_in[:] = t.unsqueeze(0) - spks_in[0] = spks - cond_in[0] = cond + x_in[:] = torch.cat([x, x], dim=0) + mask_in[:] = torch.cat([mask, mask], dim=0) + mu_in[0:B] = mu + # mu_in[B:] is zero for uncond + t_in[:] = t.expand(B * 2) + if spks is not None: + spks_in[0:B] = spks + # spks_in[B:] is zero for uncond + if cond is not None: + cond_in[0:B] = cond + # cond_in[B:] is zero for uncond + dphi_dt = self.forward_estimator( x_in, mask_in, mu_in, t_in, @@ -127,12 +134,13 @@ def forward_estimator(self, x, mask, mu, t, spks, cond): return self.estimator.forward(x, mask, mu, t, spks, cond) else: with self.lock: - self.estimator.set_input_shape('x', (2, 80, x.size(2))) - self.estimator.set_input_shape('mask', (2, 1, x.size(2))) - self.estimator.set_input_shape('mu', (2, 80, x.size(2))) - self.estimator.set_input_shape('t', (2,)) - self.estimator.set_input_shape('spks', (2, 80)) - self.estimator.set_input_shape('cond', (2, 80, x.size(2))) + B = x.size(0) + self.estimator.set_input_shape('x', (B, 80, x.size(2))) + self.estimator.set_input_shape('mask', (B, 1, x.size(2))) + self.estimator.set_input_shape('mu', (B, 80, x.size(2))) + self.estimator.set_input_shape('t', (B,)) + self.estimator.set_input_shape('spks', (B, 80)) + self.estimator.set_input_shape('cond', (B, 80, x.size(2))) # run trt engine self.estimator.execute_v2([x.contiguous().data_ptr(), mask.contiguous().data_ptr(), @@ -211,8 +219,9 @@ def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None): """ z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature + z = z.expand(mu.shape[0], -1, -1) # fix prompt and overlap part mu and z t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) if self.t_scheduler == 'cosine': t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) - return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None \ No newline at end of file diff --git a/src/chatterbox/models/s3gen/s3gen.py b/src/chatterbox/models/s3gen/s3gen.py index b1cf05e6..d07158b9 100644 --- a/src/chatterbox/models/s3gen/s3gen.py +++ b/src/chatterbox/models/s3gen/s3gen.py @@ -33,11 +33,6 @@ from .configs import CFM_PARAMS -def drop_invalid_tokens(x): - assert len(x.shape) <= 2 and x.shape[0] == 1, "only batch size of one allowed for now" - return x[x < SPEECH_VOCAB_SIZE] - - # TODO: global resampler cache @lru_cache(100) def get_resampler(src_sr, dst_sr, device): @@ -146,7 +141,7 @@ def embed_ref( "Reference mel length is not equal to 2 * reference token length.\n" ) ref_speech_tokens = ref_speech_tokens[:, :ref_mels_24.shape[1] // 2] - ref_speech_token_lens[0] = ref_speech_tokens.shape[1] + ref_speech_token_lens.fill_(ref_speech_tokens.shape[1]) return dict( prompt_token=ref_speech_tokens.to(device), @@ -159,6 +154,7 @@ def embed_ref( def forward( self, speech_tokens: torch.LongTensor, + speech_token_lens: torch.LongTensor, # locally-computed ref embedding (mutex with ref_dict) ref_wav: Optional[torch.Tensor], ref_sr: Optional[int], @@ -197,9 +193,6 @@ def forward( if len(speech_tokens.shape) == 1: speech_tokens = speech_tokens.unsqueeze(0) - # assert speech_tokens.shape[0] == 1, "only batch size of one allowed for now" - speech_token_lens = torch.LongTensor([speech_tokens.size(1)]).to(self.device) - output_mels, _ = self.flow.inference( token=speech_tokens, token_len=speech_token_lens, @@ -238,6 +231,7 @@ def __init__(self): def forward( self, speech_tokens, + speech_token_lens: torch.LongTensor, # locally-computed ref embedding (mutex with ref_dict) ref_wav: Optional[torch.Tensor], ref_sr: Optional[int], @@ -245,7 +239,7 @@ def forward( ref_dict: Optional[dict] = None, finalize: bool = False ): - output_mels = super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) + output_mels = super().forward(speech_tokens, speech_token_lens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) # TODO jrm: ignoring the speed control (mel interpolation) and the HiFTGAN caching mechanisms for now. hift_cache_source = torch.zeros(1, 1, 0).to(self.device) @@ -262,6 +256,7 @@ def forward( def flow_inference( self, speech_tokens, + speech_token_lens: torch.LongTensor, # locally-computed ref embedding (mutex with ref_dict) ref_wav: Optional[torch.Tensor] = None, ref_sr: Optional[int] = None, @@ -269,18 +264,21 @@ def flow_inference( ref_dict: Optional[dict] = None, finalize: bool = False, ): - return super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) + return super().forward(speech_tokens, speech_token_lens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) @torch.inference_mode() def hift_inference(self, speech_feat, cache_source: torch.Tensor = None): if cache_source is None: - cache_source = torch.zeros(1, 1, 0).to(self.device) + B = speech_feat.shape[0] + cache_source = torch.zeros(B, 1, 0).to(self.device) return self.mel2wav.inference(speech_feat=speech_feat, cache_source=cache_source) + @torch.inference_mode() def inference( self, speech_tokens, + speech_token_lens: Optional[torch.LongTensor] = None, # locally-computed ref embedding (mutex with ref_dict) ref_wav: Optional[torch.Tensor] = None, ref_sr: Optional[int] = None, @@ -289,10 +287,12 @@ def inference( cache_source: torch.Tensor = None, # NOTE: this arg is for streaming, it can probably be removed here finalize: bool = True, ): - output_mels = self.flow_inference(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) + if speech_token_lens is None: + speech_token_lens = torch.tensor([speech_tokens.size(1)] * speech_tokens.size(0), device=speech_tokens.device) + output_mels = self.flow_inference(speech_tokens, speech_token_lens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize) output_wavs, output_sources = self.hift_inference(output_mels, cache_source) # NOTE: ad-hoc method to reduce "spillover" from the reference clip. output_wavs[:, :len(self.trim_fade)] *= self.trim_fade - return output_wavs, output_sources + return output_wavs, output_sources \ No newline at end of file diff --git a/src/chatterbox/models/s3tokenizer/__init__.py b/src/chatterbox/models/s3tokenizer/__init__.py index cb2973ab..b4799167 100644 --- a/src/chatterbox/models/s3tokenizer/__init__.py +++ b/src/chatterbox/models/s3tokenizer/__init__.py @@ -5,26 +5,9 @@ S3_TOKEN_RATE, SPEECH_VOCAB_SIZE, S3Tokenizer, + drop_invalid_tokens, ) SOS = SPEECH_VOCAB_SIZE -EOS = SPEECH_VOCAB_SIZE + 1 - - - -def drop_invalid_tokens(x): - """Drop SoS and EoS""" - assert len(x.shape) == 1 or (len(x.shape) == 2 and x.shape[0] == 1), "only batch size of one allowed for now" - if SOS in x: - s = (x == SOS).nonzero(as_tuple=True)[0].squeeze(0) + 1 - else: - s = 0 - - if EOS in x: - e = (x == EOS).nonzero(as_tuple=True)[0].squeeze(0) - else: - e = None - - x = x[s: e] - return x +EOS = SPEECH_VOCAB_SIZE + 1 \ No newline at end of file diff --git a/src/chatterbox/models/s3tokenizer/s3tokenizer.py b/src/chatterbox/models/s3tokenizer/s3tokenizer.py index 8648608a..8fb2fc76 100644 --- a/src/chatterbox/models/s3tokenizer/s3tokenizer.py +++ b/src/chatterbox/models/s3tokenizer/s3tokenizer.py @@ -19,6 +19,17 @@ SPEECH_VOCAB_SIZE = 6561 +def drop_invalid_tokens(x: torch.Tensor) -> List[torch.Tensor]: + """ + Filters out invalid tokens from a batch of token sequences. + Invalid tokens are those with an ID >= SPEECH_VOCAB_SIZE. + Returns a list of tensors, as each sequence may have a different length after filtering. + """ + if x.dim() == 1: + x = x.unsqueeze(0) + return [row[row < SPEECH_VOCAB_SIZE] for row in x] + + class S3Tokenizer(S3TokenizerV2): """ s3tokenizer.S3TokenizerV2 with the following changes: diff --git a/src/chatterbox/models/t3/t3.py b/src/chatterbox/models/t3/t3.py index 4984470c..7078094a 100644 --- a/src/chatterbox/models/t3/t3.py +++ b/src/chatterbox/models/t3/t3.py @@ -89,7 +89,8 @@ def prepare_input_embeds( cond_emb = self.prepare_conditioning(t3_cond) # (B, len_cond, dim) text_emb = self.text_emb(text_tokens) # (B, len_text, dim) if cfg_weight > 0.0: - text_emb[1].zero_() # CFG uncond + B = text_tokens.size(0) // 2 + text_emb[B:].zero_() # CFG uncond speech_emb = self.speech_emb(speech_tokens) # (B, len_speech, dim) if self.hp.input_pos_emb == "learned": @@ -101,10 +102,7 @@ def prepare_input_embeds( cond_emb = cond_emb.expand(text_emb.size(0), -1, -1) # concat - embeds = torch.stack([ - torch.cat((ce, te, se)) - for ce, te, se in zip(cond_emb, text_emb, speech_emb) - ]) # (B, length, dim) + embeds = torch.cat([cond_emb, text_emb, speech_emb], dim=1) # (B, length, dim) return embeds, len_cond def forward( @@ -206,11 +204,9 @@ def inference( *, t3_cond: T3Cond, text_tokens: Tensor, - initial_speech_tokens: Optional[Tensor]=None, - + initial_speech_tokens: Optional[Tensor] = None, # misc conditioning - prepend_prompt_speech_tokens: Optional[Tensor]=None, - + prepend_prompt_speech_tokens: Optional[Tensor] = None, # HF generate args num_return_sequences=1, max_new_tokens=None, @@ -222,15 +218,21 @@ def inference( length_penalty=1.0, repetition_penalty=1.2, cfg_weight=0, - ): + ) -> List[torch.Tensor]: """ Args: text_tokens: a 1D (unbatched) or 2D (batched) tensor. """ # Validate / sanitize inputs assert prepend_prompt_speech_tokens is None, "not implemented" + assert num_return_sequences == 1, "num_return_sequences > 1 not implemented" _ensure_BOT_EOT(text_tokens, self.hp) text_tokens = torch.atleast_2d(text_tokens).to(dtype=torch.long, device=self.device) + B_orig = text_tokens.size(0) + if cfg_weight > 0.0: + B_orig = B_orig // 2 + + max_new_tokens = max_new_tokens or self.hp.max_speech_tokens # Default initial speech to a single start-of-speech token if initial_speech_tokens is None: @@ -262,41 +264,11 @@ def inference( self.patched_model = patched_model self.compiled = True - # # Run normal generate method, which calls our custom extended methods - # return self.patched_model.generate( - # inputs=initial_speech_tokens, - # decoder_cond=embeds, - # bos_token_id=self.hp.start_speech_token, - # eos_token_id=(self.hp.stop_speech_token if stop_on_eos else -1), - # pad_token_id=self.hp.stop_speech_token, - # max_new_tokens=max_new_tokens or self.hp.max_speech_tokens, - # num_return_sequences=num_return_sequences, - # temperature=temperature, - # min_p=min_p, - # length_penalty=length_penalty, - # repetition_penalty=repetition_penalty, - # do_sample=do_sample, - # # cache_implementation=None if not self.compiled else "static", - # ) - + inputs_embeds = embeds device = embeds.device - bos_token = torch.tensor([[self.hp.start_speech_token]], dtype=torch.long, device=device) - bos_embed = self.speech_emb(bos_token) # shape: (B, 1, embed_dim) - bos_embed = bos_embed + self.speech_pos_emb.get_fixed_embedding(0) - - # batch_size=2 for CFG - bos_embed = torch.cat([bos_embed, bos_embed]) - - # Combine condition and BOS token for the initial input if cfg_weight > 0 - if cfg_weight > 0: - inputs_embeds = torch.cat([embeds, bos_embed], dim=1) - else: - inputs_embeds = embeds - - # Track generated token ids; start with the BOS token. - generated_ids = bos_token.clone() - predicted = [] # To store the predicted tokens + # Track generated token ids; start with the initial tokens. + generated_ids = initial_speech_tokens.clone() # Instantiate the logits processors. min_p_warper = MinPLogitsWarper(min_p=min_p) @@ -316,40 +288,49 @@ def inference( past = output.past_key_values # ---- Generation Loop using kv_cache ---- + len_initial_speech = initial_speech_tokens.size(1) + + # for CFG, we only need to track the conditional part for repetition penalty and output + generated_ids_cond = generated_ids[0:B_orig] + is_finished = torch.zeros(B_orig, dtype=torch.bool, device=device) + for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True): + if is_finished.all(): + break + logits = output.logits[:, -1, :] # CFG if cfg_weight > 0.0: - logits_cond = logits[0:1] - logits_uncond = logits[1:2] + logits_cond = logits[0:B_orig] + logits_uncond = logits[B_orig:] logits = logits_cond + cfg_weight * (logits_cond - logits_uncond) - logits = logits.squeeze(1) - # Apply temperature scaling. if temperature != 1.0: logits = logits / temperature # Apply repetition penalty and top‑p filtering. - logits = repetition_penalty_processor(generated_ids, logits) + logits = repetition_penalty_processor(generated_ids_cond, logits) logits = min_p_warper(None, logits) logits = top_p_warper(None, logits) + + # For finished sequences, force sampling of the EOS token. + logits[is_finished, :] = -float("Inf") + logits[is_finished, self.hp.stop_speech_token] = 0.0 # Convert logits to probabilities and sample the next token. probs = torch.softmax(logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) # shape: (B, 1) - predicted.append(next_token) - generated_ids = torch.cat([generated_ids, next_token], dim=1) + generated_ids_cond = torch.cat([generated_ids_cond, next_token], dim=1) # Check for EOS token. - if next_token.view(-1) == self.hp.stop_speech_token: - break + is_finished |= (next_token.view(-1) == self.hp.stop_speech_token) # Get embedding for the new token. next_token_embed = self.speech_emb(next_token) - next_token_embed = next_token_embed + self.speech_pos_emb.get_fixed_embedding(i + 1) + next_token_embed = next_token_embed + self.speech_pos_emb.get_fixed_embedding(len_initial_speech + i) # For CFG if cfg_weight > 0.0: @@ -366,6 +347,14 @@ def inference( # Update the kv_cache. past = output.past_key_values - # Concatenate all predicted tokens along the sequence dimension. - predicted_tokens = torch.cat(predicted, dim=1) # shape: (B, num_tokens) - return predicted_tokens + # Un-batch and trim EOS token + output_tokens = [] + for gen_ids in generated_ids_cond: + # Remove initial speech tokens that were passed in + gen_ids = gen_ids[len_initial_speech:] + eos_idx = (gen_ids == self.hp.stop_speech_token).nonzero(as_tuple=True)[0] + if len(eos_idx) > 0: + gen_ids = gen_ids[:eos_idx[0]] + output_tokens.append(gen_ids) + + return output_tokens \ No newline at end of file diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index f22a5e70..feabfdba 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -7,6 +7,7 @@ import torch.nn.functional as F from huggingface_hub import hf_hub_download from safetensors.torch import load_file +from typing import Union, List from .models.t3 import T3 from .models.s3tokenizer import S3_SR, drop_invalid_tokens @@ -179,63 +180,102 @@ def from_pretrained(cls, device) -> 'ChatterboxTTS': return cls.from_local(Path(local_path).parent, device) - def prepare_conditionals(self, wav_fpath, exaggeration=0.5): - ## Load reference wav - s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR) + def prepare_conditionals(self, wav_fpaths: Union[str, List[str]], exaggeration=0.5): + if isinstance(wav_fpaths, str): + wav_fpaths = [wav_fpaths] - ref_16k_wav = librosa.resample(s3gen_ref_wav, orig_sr=S3GEN_SR, target_sr=S3_SR) + s3gen_ref_wavs, ref_16k_wavs_np = [], [] + for fpath in wav_fpaths: + s3gen_wav, _ = librosa.load(fpath, sr=S3GEN_SR) + s3gen_wav_tensor = torch.from_numpy(s3gen_wav[:self.DEC_COND_LEN]) + s3gen_ref_wavs.append(s3gen_wav_tensor) - s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN] - s3gen_ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device) + ref_16k_wav, _ = librosa.load(fpath, sr=S3_SR) + ref_16k_wavs_np.append(ref_16k_wav) + + s3gen_ref_batch = torch.nn.utils.rnn.pad_sequence(s3gen_ref_wavs, batch_first=True) + s3gen_ref_dict = self.s3gen.embed_ref(s3gen_ref_batch, S3GEN_SR, device=self.device) # Speech cond prompt tokens if plen := self.t3.hp.speech_cond_prompt_len: s3_tokzr = self.s3gen.tokenizer - t3_cond_prompt_tokens, _ = s3_tokzr.forward([ref_16k_wav[:self.ENC_COND_LEN]], max_len=plen) - t3_cond_prompt_tokens = torch.atleast_2d(t3_cond_prompt_tokens).to(self.device) + ref_16k_prompts = [wav[:self.ENC_COND_LEN] for wav in ref_16k_wavs_np] + t3_cond_prompt_tokens, _ = s3_tokzr.forward(ref_16k_prompts, max_len=plen) + t3_cond_prompt_tokens = t3_cond_prompt_tokens.to(self.device) + else: + t3_cond_prompt_tokens = None # Voice-encoder speaker embedding - ve_embed = torch.from_numpy(self.ve.embeds_from_wavs([ref_16k_wav], sample_rate=S3_SR)) - ve_embed = ve_embed.mean(axis=0, keepdim=True).to(self.device) + ve_embeds = self.ve.embeds_from_wavs(ref_16k_wavs_np, sample_rate=S3_SR) + ve_embed = torch.from_numpy(ve_embeds).unsqueeze(1).to(self.device) + batch_size = len(wav_fpaths) t3_cond = T3Cond( speaker_emb=ve_embed, cond_prompt_speech_tokens=t3_cond_prompt_tokens, - emotion_adv=exaggeration * torch.ones(1, 1, 1), + emotion_adv=exaggeration * torch.ones(batch_size, 1, 1), ).to(device=self.device) self.conds = Conditionals(t3_cond, s3gen_ref_dict) def generate( self, - text, + text: Union[str, List[str]], repetition_penalty=1.2, min_p=0.05, top_p=1.0, - audio_prompt_path=None, + audio_prompt_path: Union[str, List[str]] = None, exaggeration=0.5, cfg_weight=0.5, temperature=0.8, - ): + ) -> Union[torch.Tensor, List[torch.Tensor]]: + is_single_input = isinstance(text, str) + if is_single_input: + text = [text] + batch_size = len(text) + if audio_prompt_path: self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) else: assert self.conds is not None, "Please `prepare_conditionals` first or specify `audio_prompt_path`" - # Update exaggeration if needed + # Broadcast conditioning if a single prompt is used for a batch of texts + current_cond_bs = self.conds.t3.speaker_emb.size(0) + if current_cond_bs == 1 and batch_size > 1: + t3c = self.conds.t3 + t3c.speaker_emb = t3c.speaker_emb.expand(batch_size, -1, -1) + if t3c.cond_prompt_speech_tokens is not None: + t3c.cond_prompt_speech_tokens = t3c.cond_prompt_speech_tokens.expand(batch_size, -1) + if t3c.emotion_adv is not None: + t3c.emotion_adv = t3c.emotion_adv.expand(batch_size, -1, -1) + + gend = self.conds.gen + for k, v in gend.items(): + if torch.is_tensor(v): + if k.endswith("_len"): + gend[k] = v.expand(batch_size) + else: + gend[k] = v.expand(batch_size, *v.shape[1:]) + elif current_cond_bs != batch_size and not (current_cond_bs == 1 and batch_size == 1): + raise ValueError(f"Mismatch between number of texts ({batch_size}) and audio prompts ({current_cond_bs})") + + # Update exaggeration if needed (applied to the whole batch) if exaggeration != self.conds.t3.emotion_adv[0, 0, 0]: - _cond: T3Cond = self.conds.t3 - self.conds.t3 = T3Cond( - speaker_emb=_cond.speaker_emb, - cond_prompt_speech_tokens=_cond.cond_prompt_speech_tokens, - emotion_adv=exaggeration * torch.ones(1, 1, 1), - ).to(device=self.device) + self.conds.t3.emotion_adv = exaggeration * torch.ones(batch_size, 1, 1, device=self.device) # Norm and tokenize text - text = punc_norm(text) - text_tokens = self.tokenizer.text_to_tokens(text).to(self.device) + texts = [punc_norm(t) for t in text] + tokenized_texts = [self.tokenizer.text_to_tokens(t).squeeze(0) for t in texts] + text_tokens = torch.nn.utils.rnn.pad_sequence(tokenized_texts, batch_first=True, padding_value=0).to(self.device) + t3_cond_for_inference = self.conds.t3 if cfg_weight > 0.0: - text_tokens = torch.cat([text_tokens, text_tokens], dim=0) # Need two seqs for CFG + # Duplicate text tokens and conditioning tensors for CFG + text_tokens = torch.cat([text_tokens, text_tokens], dim=0) + t3_cond_for_inference = T3Cond( + speaker_emb=torch.cat([self.conds.t3.speaker_emb] * 2), + cond_prompt_speech_tokens=torch.cat([self.conds.t3.cond_prompt_speech_tokens] * 2) if self.conds.t3.cond_prompt_speech_tokens is not None else None, + emotion_adv=torch.cat([self.conds.t3.emotion_adv] * 2) if self.conds.t3.emotion_adv is not None else None, + ) sot = self.t3.hp.start_text_token eot = self.t3.hp.stop_text_token @@ -243,29 +283,32 @@ def generate( text_tokens = F.pad(text_tokens, (0, 1), value=eot) with torch.inference_mode(): - speech_tokens = self.t3.inference( - t3_cond=self.conds.t3, + # T3 generates a list of variable-length token sequences + speech_tokens_list = self.t3.inference( + t3_cond=t3_cond_for_inference, text_tokens=text_tokens, - max_new_tokens=1000, # TODO: use the value in config + max_new_tokens=1000, temperature=temperature, cfg_weight=cfg_weight, repetition_penalty=repetition_penalty, min_p=min_p, top_p=top_p, ) - # Extract only the conditional batch. - speech_tokens = speech_tokens[0] - - # TODO: output becomes 1D - speech_tokens = drop_invalid_tokens(speech_tokens) - - speech_tokens = speech_tokens[speech_tokens < 6561] - speech_tokens = speech_tokens.to(self.device) + # Pad for filtering, filter, and pad again for S3Gen + speech_tokens_padded = torch.nn.utils.rnn.pad_sequence(speech_tokens_list, batch_first=True, padding_value=self.t3.hp.stop_speech_token) + clean_tokens_list = drop_invalid_tokens(speech_tokens_padded) + s3gen_tokens_padded = torch.nn.utils.rnn.pad_sequence(clean_tokens_list, batch_first=True, padding_value=0) + s3gen_token_lens = torch.tensor([len(t) for t in clean_tokens_list], device=self.device) - wav, _ = self.s3gen.inference( - speech_tokens=speech_tokens, + wavs, _ = self.s3gen.inference( + speech_tokens=s3gen_tokens_padded.to(self.device), + speech_token_lens=s3gen_token_lens, ref_dict=self.conds.gen, ) - wav = wav.squeeze(0).detach().cpu().numpy() - return torch.from_numpy(wav).unsqueeze(0) \ No newline at end of file + output_wavs = [wav.cpu().numpy() for wav in wavs] + + output_tensors = [torch.from_numpy(wav).unsqueeze(0) for wav in output_wavs] + if is_single_input: + return output_tensors[0] + return output_tensors \ No newline at end of file diff --git a/src/chatterbox/vc.py b/src/chatterbox/vc.py index 14fd4553..eee15e24 100644 --- a/src/chatterbox/vc.py +++ b/src/chatterbox/vc.py @@ -5,6 +5,7 @@ import perth from huggingface_hub import hf_hub_download from safetensors.torch import load_file +from typing import List, Union from .models.s3tokenizer import S3_SR from .models.s3gen import S3GEN_SR, S3Gen @@ -21,7 +22,7 @@ def __init__( self, s3gen: S3Gen, device: str, - ref_dict: dict=None, + ref_dict: dict = None, ): self.sr = S3GEN_SR self.s3gen = s3gen @@ -38,13 +39,13 @@ def __init__( @classmethod def from_local(cls, ckpt_dir, device) -> 'ChatterboxVC': ckpt_dir = Path(ckpt_dir) - + # Always load to CPU first for non-CUDA devices to handle CUDA-saved models if device in ["cpu", "mps"]: map_location = torch.device('cpu') else: map_location = None - + ref_dict = None if (builtin_voice := ckpt_dir / "conds.pt").exists(): states = torch.load(builtin_voice, map_location=map_location) @@ -67,37 +68,69 @@ def from_pretrained(cls, device) -> 'ChatterboxVC': else: print("MPS not available because the current MacOS version is not 12.3+ and/or you do not have an MPS-enabled device on this machine.") device = "cpu" - + for fpath in ["s3gen.safetensors", "conds.pt"]: local_path = hf_hub_download(repo_id=REPO_ID, filename=fpath) return cls.from_local(Path(local_path).parent, device) - def set_target_voice(self, wav_fpath): - ## Load reference wav - s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR) - - s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN] - self.ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device) + def set_target_voice(self, wav_fpaths: Union[str, List[str]]): + if isinstance(wav_fpaths, str): + wav_fpaths = [wav_fpaths] + + s3gen_ref_wavs = [] + for fpath in wav_fpaths: + s3gen_ref_wav, _ = librosa.load(fpath, sr=S3GEN_SR) + s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN] + s3gen_ref_wavs.append(torch.from_numpy(s3gen_ref_wav)) + + s3gen_ref_batch = torch.nn.utils.rnn.pad_sequence(s3gen_ref_wavs, batch_first=True) + self.ref_dict = self.s3gen.embed_ref(s3gen_ref_batch, S3GEN_SR, device=self.device) def generate( self, - audio, - target_voice_path=None, - ): + audio: Union[str, List[str]], + target_voice_path: Union[str, List[str]] = None, + ) -> Union[torch.Tensor, List[torch.Tensor]]: + is_single_input = isinstance(audio, str) + if is_single_input: + audio = [audio] + batch_size = len(audio) + if target_voice_path: self.set_target_voice(target_voice_path) else: - assert self.ref_dict is not None, "Please `prepare_conditionals` first or specify `target_voice_path`" + assert self.ref_dict is not None, "Please call `set_target_voice` first or specify `target_voice_path`" + + # Broadcast conditioning if a single prompt is used for a batch of inputs + current_cond_bs = self.ref_dict['embedding'].size(0) + if current_cond_bs == 1 and batch_size > 1: + for k, v in self.ref_dict.items(): + if torch.is_tensor(v): + if k.endswith("_len"): + self.ref_dict[k] = v.expand(batch_size) + else: + self.ref_dict[k] = v.expand(batch_size, *v.shape[1:]) + elif current_cond_bs != batch_size and not (current_cond_bs == 1 and batch_size == 1): + raise ValueError(f"Mismatch between number of source audios ({batch_size}) and target voice paths ({current_cond_bs})") with torch.inference_mode(): - audio_16, _ = librosa.load(audio, sr=S3_SR) - audio_16 = torch.from_numpy(audio_16).float().to(self.device)[None, ] + audios_16k = [] + for a in audio: + audio_16, _ = librosa.load(a, sr=S3_SR) + audios_16k.append(torch.from_numpy(audio_16).float()) + + audio_16_padded = torch.nn.utils.rnn.pad_sequence(audios_16k, batch_first=True).to(self.device) - s3_tokens, _ = self.s3gen.tokenizer(audio_16) - wav, _ = self.s3gen.inference( + s3_tokens, s3_token_lens = self.s3gen.tokenizer(audio_16_padded) + wavs, _ = self.s3gen.inference( speech_tokens=s3_tokens, + speech_token_lens=s3_token_lens, ref_dict=self.ref_dict, ) - wav = wav.squeeze(0).detach().cpu().numpy() - return torch.from_numpy(wav).unsqueeze(0) \ No newline at end of file + output_wavs = [wav.detach().cpu().numpy() for wav in wavs] + + output_tensors = [torch.from_numpy(wav).unsqueeze(0) for wav in output_wavs] + if is_single_input: + return output_tensors[0] + return output_tensors \ No newline at end of file From 73188c5c8c907434b065304db3f58b92365693b0 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 07:46:21 +0300 Subject: [PATCH 03/31] batch: preventing the noise at the end of shorter sequences --- .gitignore | 2 ++ src/chatterbox/models/s3gen/const.py | 6 ++++++ src/chatterbox/tts.py | 8 ++++++-- src/chatterbox/vc.py | 9 +++++++-- 4 files changed, 21 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 8e683ebf..aaf1c466 100644 --- a/.gitignore +++ b/.gitignore @@ -50,3 +50,5 @@ checkpoints/ /.vs .vs/slnx.sqlite /.vs/chatterbox/CopilotIndices +/.vs +/.vs diff --git a/src/chatterbox/models/s3gen/const.py b/src/chatterbox/models/s3gen/const.py index 72de6a23..c75f5488 100644 --- a/src/chatterbox/models/s3gen/const.py +++ b/src/chatterbox/models/s3gen/const.py @@ -1 +1,7 @@ S3GEN_SR = 24000 + +# The ratio of audio samples to speech tokens. +# 1 token -> 2 mel frames (in CausalMaskedDiffWithXvec) +# 1 mel frame -> 480 audio samples (hop_size in hifigan) +# Total: 2 * 480 = 960 +TOKEN_TO_WAV_RATIO = 960 \ No newline at end of file diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index feabfdba..562f7e7c 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -12,6 +12,7 @@ from .models.t3 import T3 from .models.s3tokenizer import S3_SR, drop_invalid_tokens from .models.s3gen import S3GEN_SR, S3Gen +from .models.s3gen.const import TOKEN_TO_WAV_RATIO from .models.tokenizers import EnTokenizer from .models.voice_encoder import VoiceEncoder from .models.t3.modules.cond_enc import T3Cond @@ -306,9 +307,12 @@ def generate( speech_token_lens=s3gen_token_lens, ref_dict=self.conds.gen, ) - output_wavs = [wav.cpu().numpy() for wav in wavs] + # Trim padding noise + audio_lengths = s3gen_token_lens * TOKEN_TO_WAV_RATIO + output_tensors = [] + for i, wav in enumerate(wavs): + output_tensors.append(wav[:audio_lengths[i]].cpu().unsqueeze(0)) - output_tensors = [torch.from_numpy(wav).unsqueeze(0) for wav in output_wavs] if is_single_input: return output_tensors[0] return output_tensors \ No newline at end of file diff --git a/src/chatterbox/vc.py b/src/chatterbox/vc.py index eee15e24..e2902ca0 100644 --- a/src/chatterbox/vc.py +++ b/src/chatterbox/vc.py @@ -9,6 +9,7 @@ from .models.s3tokenizer import S3_SR from .models.s3gen import S3GEN_SR, S3Gen +from .models.s3gen.const import TOKEN_TO_WAV_RATIO REPO_ID = "ResembleAI/chatterbox" @@ -17,6 +18,7 @@ class ChatterboxVC: ENC_COND_LEN = 6 * S3_SR DEC_COND_LEN = 10 * S3GEN_SR + TOKEN_TO_WAV_RATIO = 960 def __init__( self, @@ -128,9 +130,12 @@ def generate( speech_token_lens=s3_token_lens, ref_dict=self.ref_dict, ) - output_wavs = [wav.detach().cpu().numpy() for wav in wavs] + # Trim padding noise + audio_lengths = s3_token_lens * TOKEN_TO_WAV_RATIO + output_tensors = [] + for i, wav in enumerate(wavs): + output_tensors.append(wav[:audio_lengths[i]].cpu().unsqueeze(0)) - output_tensors = [torch.from_numpy(wav).unsqueeze(0) for wav in output_wavs] if is_single_input: return output_tensors[0] return output_tensors \ No newline at end of file From e1f4fccbd9ae95495b37cfd5bf90207aaf4b2ab4 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 08:28:45 +0300 Subject: [PATCH 04/31] batching: implemented variations + improved the example_tts --- example_tts.py | 43 ++++++++++++++++++++-------------- example_vc_batching.py | 41 ++++++++++++++++++++++++++++++++ src/chatterbox/models/t3/t3.py | 5 ++-- src/chatterbox/tts.py | 37 +++++++++++++++++++++-------- 4 files changed, 96 insertions(+), 30 deletions(-) create mode 100644 example_vc_batching.py diff --git a/example_tts.py b/example_tts.py index ec7c9ab0..6fbfff83 100644 --- a/example_tts.py +++ b/example_tts.py @@ -14,23 +14,32 @@ model = ChatterboxTTS.from_pretrained(device=device) -text = ["Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", - "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", - "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill.", - "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill."] -wavs = model.generate(text) +text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus." +wav = model.generate(text) +ta.save("test-default-voice.wav", wav, model.sr) -# Save each generated waveform to a separate file -for i, wav in enumerate(wavs): - ta.save(f"test-batch-{i+1}.wav", wav, model.sr) - -# If you want to synthesize with a different voice, specify the audio prompt +# If you want to synthesize with a different voice, specify the audio prompt: AUDIO_PROMPT_PATH = "C:\\_myDrive\\repos\\auto-vlog\\assets\\audio_sample1.wav" -try: - wavs = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) - for i, wav in enumerate(wavs): - ta.save(f"test-prompted-batch-{i+1}.wav", wav, model.sr) -except FileNotFoundError: - print(f"\nSkipping second example because audio prompt not found at: '{AUDIO_PROMPT_PATH}'") - print("Please replace 'YOUR_FILE.wav' with a real audio file path to test this.") + + +# Batching - list of strings to synthesize multiple different texts in a single batch. +# This is the most efficient way to process multiple, different prompts at once. +# Careful: 1 text = 1 additional KV Cache (Vram) +texts_batch = [ "This is the first sentence to be synthesized in a batch.", + "This is the second one." ] + +wavs_batch = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH) +for i, wav in enumerate(wavs_batch): + ta.save(f"test-batch-output-{i+1}.wav", wav, model.sr) + + +# Batching - Use num_return_sequences to generate multiple variations for each text. +# This is highly efficient for creating diverse samples, as the prompt is only processed once. +# Without making extra KV Caches. +num_variations = 3 + +wavs_batch_multi = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH, num_return_sequences=num_variations) +for i, group in enumerate(wavs_batch_multi): + for j, wav in enumerate(group): + ta.save(f"test-batch-{i+1}-multi-output-{j+1}.wav", wav, model.sr) diff --git a/example_vc_batching.py b/example_vc_batching.py new file mode 100644 index 00000000..e164d6f1 --- /dev/null +++ b/example_vc_batching.py @@ -0,0 +1,41 @@ +import torchaudio as ta +import torch +from chatterbox.tts import ChatterboxTTS + +# Automatically detect the best available device +if torch.cuda.is_available(): + device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" +else: + device = "cpu" + +print(f"Using device: {device}") + + +model = ChatterboxTTS.from_pretrained(device=device) + +texts_batch = [ "This is the first sentence to be synthesized in a batch.", + "This is the second one." ] + + +# If you want to synthesize with a different voice, specify the audio prompt: +AUDIO_PROMPT_PATH = "YOUR_AUDIO_PROMPT.wav" + + +# Batching - list of strings to synthesize multiple different texts in a single batch. +# This is the most efficient way to process multiple, different prompts at once. +# Careful: 1 text = 1 additional KV Cache (Vram) +wavs_batch = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH) +for i, wav in enumerate(wavs_batch): + ta.save(f"test-batch-output-{i+1}.wav", wav, model.sr) + +# Batching - Use num_return_sequences to generate multiple variations for each text. +# This is highly efficient for creating diverse samples, as the prompt is only processed once. +# Without making extra KV Caches. +num_variations = 3 + +wavs_batch_multi = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH, num_return_sequences=num_variations) +for i, group in enumerate(wavs_batch_multi): + for j, wav in enumerate(group): + ta.save(f"test-batch-{i+1}-multi-output-{j+1}.wav", wav, model.sr) \ No newline at end of file diff --git a/src/chatterbox/models/t3/t3.py b/src/chatterbox/models/t3/t3.py index 7078094a..a3b66187 100644 --- a/src/chatterbox/models/t3/t3.py +++ b/src/chatterbox/models/t3/t3.py @@ -204,9 +204,9 @@ def inference( *, t3_cond: T3Cond, text_tokens: Tensor, - initial_speech_tokens: Optional[Tensor] = None, + initial_speech_tokens: Optional[Tensor]=None, # misc conditioning - prepend_prompt_speech_tokens: Optional[Tensor] = None, + prepend_prompt_speech_tokens: Optional[Tensor]=None, # HF generate args num_return_sequences=1, max_new_tokens=None, @@ -225,7 +225,6 @@ def inference( """ # Validate / sanitize inputs assert prepend_prompt_speech_tokens is None, "not implemented" - assert num_return_sequences == 1, "num_return_sequences > 1 not implemented" _ensure_BOT_EOT(text_tokens, self.hp) text_tokens = torch.atleast_2d(text_tokens).to(dtype=torch.long, device=self.device) B_orig = text_tokens.size(0) diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 562f7e7c..2198e1b8 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -184,7 +184,8 @@ def from_pretrained(cls, device) -> 'ChatterboxTTS': def prepare_conditionals(self, wav_fpaths: Union[str, List[str]], exaggeration=0.5): if isinstance(wav_fpaths, str): wav_fpaths = [wav_fpaths] - + + ## Load reference wav s3gen_ref_wavs, ref_16k_wavs_np = [], [] for fpath in wav_fpaths: s3gen_wav, _ = librosa.load(fpath, sr=S3GEN_SR) @@ -228,7 +229,8 @@ def generate( exaggeration=0.5, cfg_weight=0.5, temperature=0.8, - ) -> Union[torch.Tensor, List[torch.Tensor]]: + num_return_sequences=1, + ) -> Union[torch.Tensor, List[torch.Tensor], List[List[torch.Tensor]]]: is_single_input = isinstance(text, str) if is_single_input: text = [text] @@ -268,15 +270,22 @@ def generate( tokenized_texts = [self.tokenizer.text_to_tokens(t).squeeze(0) for t in texts] text_tokens = torch.nn.utils.rnn.pad_sequence(tokenized_texts, batch_first=True, padding_value=0).to(self.device) - t3_cond_for_inference = self.conds.t3 + # --- Start: Logic for num_return_sequences and CFG --- + t3_cond = self.conds.t3 + gen_cond = self.conds.gen + + # Expand inputs for num_return_sequences + if num_return_sequences > 1: + text_tokens = text_tokens.repeat_interleave(num_return_sequences, dim=0) + t3_cond = T3Cond(**{k: v.repeat_interleave(num_return_sequences, dim=0) if torch.is_tensor(v) else v for k, v in t3_cond.__dict__.items()}) + gen_cond = {k: v.repeat_interleave(num_return_sequences, dim=0) if torch.is_tensor(v) else v for k, v in gen_cond.items()} + if cfg_weight > 0.0: # Duplicate text tokens and conditioning tensors for CFG text_tokens = torch.cat([text_tokens, text_tokens], dim=0) - t3_cond_for_inference = T3Cond( - speaker_emb=torch.cat([self.conds.t3.speaker_emb] * 2), - cond_prompt_speech_tokens=torch.cat([self.conds.t3.cond_prompt_speech_tokens] * 2) if self.conds.t3.cond_prompt_speech_tokens is not None else None, - emotion_adv=torch.cat([self.conds.t3.emotion_adv] * 2) if self.conds.t3.emotion_adv is not None else None, - ) + t3_cond = T3Cond(**{k: torch.cat([v, v], dim=0) if torch.is_tensor(v) else v for k, v in t3_cond.__dict__.items()}) + # --- End: Logic for num_return_sequences and CFG --- + sot = self.t3.hp.start_text_token eot = self.t3.hp.stop_text_token @@ -286,7 +295,7 @@ def generate( with torch.inference_mode(): # T3 generates a list of variable-length token sequences speech_tokens_list = self.t3.inference( - t3_cond=t3_cond_for_inference, + t3_cond=t3_cond, text_tokens=text_tokens, max_new_tokens=1000, temperature=temperature, @@ -305,14 +314,22 @@ def generate( wavs, _ = self.s3gen.inference( speech_tokens=s3gen_tokens_padded.to(self.device), speech_token_lens=s3gen_token_lens, - ref_dict=self.conds.gen, + ref_dict=gen_cond, ) + # Trim padding noise audio_lengths = s3gen_token_lens * TOKEN_TO_WAV_RATIO output_tensors = [] for i, wav in enumerate(wavs): output_tensors.append(wav[:audio_lengths[i]].cpu().unsqueeze(0)) + if num_return_sequences > 1: + # Group the flat list of outputs into a list of lists + grouped_outputs = [output_tensors[i:i + num_return_sequences] for i in range(0, len(output_tensors), num_return_sequences)] + if is_single_input: + return grouped_outputs[0] + return grouped_outputs + if is_single_input: return output_tensors[0] return output_tensors \ No newline at end of file From b1e226de1dfac6a6cad75cd749ab173d2b55f9e8 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 08:36:12 +0300 Subject: [PATCH 05/31] restored watermarking --- example_tts.py | 4 ++-- example_vc_batching.py | 4 ++-- src/chatterbox/tts.py | 4 +++- src/chatterbox/vc.py | 4 +++- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/example_tts.py b/example_tts.py index 6fbfff83..487ca861 100644 --- a/example_tts.py +++ b/example_tts.py @@ -31,7 +31,7 @@ wavs_batch = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH) for i, wav in enumerate(wavs_batch): - ta.save(f"test-batch-output-{i+1}.wav", wav, model.sr) + ta.save(f"test-batch-{i+1}.wav", wav, model.sr) # Batching - Use num_return_sequences to generate multiple variations for each text. @@ -42,4 +42,4 @@ wavs_batch_multi = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH, num_return_sequences=num_variations) for i, group in enumerate(wavs_batch_multi): for j, wav in enumerate(group): - ta.save(f"test-batch-{i+1}-multi-output-{j+1}.wav", wav, model.sr) + ta.save(f"test-batch-{i+1}-variant-{j+1}.wav", wav, model.sr) diff --git a/example_vc_batching.py b/example_vc_batching.py index e164d6f1..a283bc57 100644 --- a/example_vc_batching.py +++ b/example_vc_batching.py @@ -28,7 +28,7 @@ # Careful: 1 text = 1 additional KV Cache (Vram) wavs_batch = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH) for i, wav in enumerate(wavs_batch): - ta.save(f"test-batch-output-{i+1}.wav", wav, model.sr) + ta.save(f"test-batch-{i+1}.wav", wav, model.sr) # Batching - Use num_return_sequences to generate multiple variations for each text. # This is highly efficient for creating diverse samples, as the prompt is only processed once. @@ -38,4 +38,4 @@ wavs_batch_multi = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH, num_return_sequences=num_variations) for i, group in enumerate(wavs_batch_multi): for j, wav in enumerate(group): - ta.save(f"test-batch-{i+1}-multi-output-{j+1}.wav", wav, model.sr) \ No newline at end of file + ta.save(f"test-batch-{i+1}-variant-{j+1}.wav", wav, model.sr) \ No newline at end of file diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 2198e1b8..d11fb527 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -321,7 +321,9 @@ def generate( audio_lengths = s3gen_token_lens * TOKEN_TO_WAV_RATIO output_tensors = [] for i, wav in enumerate(wavs): - output_tensors.append(wav[:audio_lengths[i]].cpu().unsqueeze(0)) + trimmed_wav = wav[:audio_lengths[i]].cpu().numpy() + watermarked_wav = self.watermarker.apply_watermark(trimmed_wav, sample_rate=self.sr) + output_tensors.append(torch.from_numpy(watermarked_wav).unsqueeze(0)) if num_return_sequences > 1: # Group the flat list of outputs into a list of lists diff --git a/src/chatterbox/vc.py b/src/chatterbox/vc.py index e2902ca0..16b038bd 100644 --- a/src/chatterbox/vc.py +++ b/src/chatterbox/vc.py @@ -134,7 +134,9 @@ def generate( audio_lengths = s3_token_lens * TOKEN_TO_WAV_RATIO output_tensors = [] for i, wav in enumerate(wavs): - output_tensors.append(wav[:audio_lengths[i]].cpu().unsqueeze(0)) + trimmed_wav = wav[:audio_lengths[i]].cpu().numpy() + watermarked_wav = self.watermarker.apply_watermark(trimmed_wav, sample_rate=self.sr) + output_tensors.append(torch.from_numpy(watermarked_wav).unsqueeze(0)) if is_single_input: return output_tensors[0] From 217de7639d07850b6bd1c915b356f8abbac47da7 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 08:44:14 +0300 Subject: [PATCH 06/31] Delete .vs/ProjectSettings.json (meta file) --- .vs/ProjectSettings.json | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 .vs/ProjectSettings.json diff --git a/.vs/ProjectSettings.json 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exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +syn_out/ +checkpoints/ +.gradio + +# Ignore generated sample .wav files +**/*.wav \ No newline at end of file From c1c9546853a81a4d9232ab275c149aa72d9b5093 Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 08:49:26 +0300 Subject: [PATCH 11/31] removed .vs folder (meta files of visual studio) --- .../17.13.441.19478/SemanticSymbols.db | Bin 77824 -> 0 bytes .../00f83e7b-4ad3-44e2-bb09-be9335e3c573.vsidx | Bin 234630 -> 0 bytes .vs/chatterbox/v17/.wsuo | Bin 35840 -> 0 bytes .vs/slnx.sqlite | Bin 90112 -> 0 bytes 4 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .vs/chatterbox/CopilotIndices/17.13.441.19478/SemanticSymbols.db delete mode 100644 .vs/chatterbox/FileContentIndex/00f83e7b-4ad3-44e2-bb09-be9335e3c573.vsidx delete mode 100644 .vs/chatterbox/v17/.wsuo delete mode 100644 .vs/slnx.sqlite diff --git 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z{0Z6RG4*}o9iR2 z$A2i#Pj7QeGo%lXKkhsDs)IiC-@>@sSJWAO*Bh6$vT`R) z!GUwmq|t4?mb-0-efR0@!IO@{9!)u{OBv$y_`QcscGp$!U|rWU_C}0sKN!TeRzE^s zdJ5epxAz!dDoqx$TVo!(_oAQPdeF40m=+u2ws%3AL*Q(E#;Me(+_sLkmuXxbo@li? z6wE9BC$8)w3)nXtGF_u_A*}%Z)@z(2jY{2AX;j?YjH%V3nyOu5n-?t2xo+Yzdy9A8 z%3@jHJLNXVaMf2b%H6o$PRjM}q_oSx&g=M_Sk^QzHksrF*!T+}jX zwA9bC0qBMy*tL_<)6$w$DTtNE4n})+Ho9$9I~r{&wc>wjh_`z~A^K_EZi$g}8>U^` zg}ch6ZOp6M*1)}iyI5uaLh!YBf00e*l5C8(N3<0?Re`UBst3Ut< z00AHX1b_e#00KY&2mk>f00izN0r>sDJ4qYb0s=q)2mk>f00e*l5C8%|00;m9An?i% kfb;)XhC8$h1b_e#00KY&2mk>f00e*l5C8%|;7$_wKWwe5fdBvi From e97127e69f0e6c232cc7a1df60e7a63d3a023caa Mon Sep 17 00:00:00 2001 From: Igor Aherne Date: Mon, 21 Jul 2025 21:25:43 +0300 Subject: [PATCH 12/31] fix(t3): Resolve memory leak in generation loop by pre-allocating token tensor --- src/chatterbox/models/t3/t3.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/src/chatterbox/models/t3/t3.py b/src/chatterbox/models/t3/t3.py index a3b66187..c120b545 100644 --- a/src/chatterbox/models/t3/t3.py +++ b/src/chatterbox/models/t3/t3.py @@ -290,8 +290,13 @@ def inference( len_initial_speech = initial_speech_tokens.size(1) # for CFG, we only need to track the conditional part for repetition penalty and output - generated_ids_cond = generated_ids[0:B_orig] + # Pre-allocate tensor to avoid memory leak from torch.cat + max_len = len_initial_speech + max_new_tokens + generated_ids_cond = torch.full((B_orig, max_len), 0, dtype=torch.long, device=device) + generated_ids_cond[:, :len_initial_speech] = generated_ids[0:B_orig] + is_finished = torch.zeros(B_orig, dtype=torch.bool, device=device) + current_token_idx = len_initial_speech for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True): if is_finished.all(): @@ -310,7 +315,8 @@ def inference( logits = logits / temperature # Apply repetition penalty and top‑p filtering. - logits = repetition_penalty_processor(generated_ids_cond, logits) + # Pass only the generated part to the repetition penalty processor + logits = repetition_penalty_processor(generated_ids_cond[:, :current_token_idx], logits) logits = min_p_warper(None, logits) logits = top_p_warper(None, logits) @@ -322,7 +328,9 @@ def inference( probs = torch.softmax(logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) # shape: (B, 1) - generated_ids_cond = torch.cat([generated_ids_cond, next_token], dim=1) + # Update the pre-allocated tensor in-place + generated_ids_cond[:, current_token_idx] = next_token.squeeze(-1) + current_token_idx += 1 # Check for EOS token. is_finished |= (next_token.view(-1) == self.hp.stop_speech_token) From 7bfc6814080ecaee3a7f01665f6e57262f6173c2 Mon Sep 17 00:00:00 2001 From: Ollie McCarthy Date: Fri, 1 Aug 2025 12:22:29 +0200 Subject: [PATCH 13/31] Update README.md with citation (#213) --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index 162ac079..ce11324d 100644 --- a/README.md +++ b/README.md @@ -113,5 +113,16 @@ print(f"Extracted watermark: {watermark}") 👋 Join us on [Discord](https://discord.gg/rJq9cRJBJ6) and let's build something awesome together! +# Citation +If you find this model useful, please consider citing. +``` +@misc{chatterboxtts2025, + author = {{Resemble AI}}, + title = {{Chatterbox-TTS}}, + year = {2025}, + howpublished = {\url{https://github.com/resemble-ai/chatterbox}}, + note = {GitHub repository} +} +``` # Disclaimer Don't use this model to do bad things. Prompts are sourced from freely available data on the internet. From a9742ee281c4cb8a47a0e88d4576c11869de2f2c Mon Sep 17 00:00:00 2001 From: zihanjin428 Date: Thu, 4 Sep 2025 11:04:22 -0400 Subject: [PATCH 14/31] Multilingual implementation (#229) * Add multilingual support * Update model card and README * Update examples and README * Remove EOS supression * Keep EOS supression for longer text * Update README.md * Update README.md * modify tokenizers, set alignment analyzer default to none * minor fixes, strict forcing EOS * Update model repo --- Chatterbox-Multilingual.png | Bin 0 -> 724392 bytes README.md | 36 +- example_tts.py | 9 +- multilingual_app.py | 319 ++++++++++++++++++ pyproject.toml | 4 +- src/chatterbox/__init__.py | 1 + src/chatterbox/models/s3gen/flow.py | 14 +- src/chatterbox/models/s3gen/utils/mel.py | 12 +- .../t3/inference/alignment_stream_analyzer.py | 88 +++-- src/chatterbox/models/t3/modules/t3_config.py | 46 ++- src/chatterbox/models/t3/t3.py | 77 +++-- src/chatterbox/models/tokenizers/__init__.py | 2 +- 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resemble-logo-horizontal -We're excited to introduce Chatterbox, [Resemble AI's](https://resemble.ai) first production-grade open source TTS model. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations. +We're excited to introduce **Chatterbox Multilingual**, [Resemble AI's](https://resemble.ai) first production-grade open source TTS model supporting **23 languages** out of the box. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations. -Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support **emotion exaggeration control**, a powerful feature that makes your voices stand out. Try it now on our [Hugging Face Gradio app.](https://huggingface.co/spaces/ResembleAI/Chatterbox) +Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life across languages. It's also the first open source TTS model to support **emotion exaggeration control** with robust **multilingual zero-shot voice cloning**. Try it now on our [Hugging Face Gradio app.](https://huggingface.co/spaces/ResembleAI/Chatterbox) If you like the model but need to scale or tune it for higher accuracy, check out our competitively priced TTS service (link). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media. # Key Details -- SoTA zeroshot TTS +- Multilingual, zero-shot TTS supporting 23 languages +- SoTA zeroshot English TTS - 0.5B Llama backbone - Unique exaggeration/intensity control - Ultra-stable with alignment-informed inference @@ -26,9 +28,12 @@ If you like the model but need to scale or tune it for higher accuracy, check ou - Easy voice conversion script - [Outperforms ElevenLabs](https://podonos.com/resembleai/chatterbox) +# Supported Languages +Arabic (ar) • Danish (da) • German (de) • Greek (el) • English (en) • Spanish (es) • Finnish (fi) • French (fr) • Hebrew (he) • Hindi (hi) • Italian (it) • Japanese (ja) • Korean (ko) • Malay (ms) • Dutch (nl) • Norwegian (no) • Polish (pl) • Portuguese (pt) • Russian (ru) • Swedish (sv) • Swahili (sw) • Turkish (tr) • Chinese (zh) # Tips - **General Use (TTS and Voice Agents):** - - The default settings (`exaggeration=0.5`, `cfg_weight=0.5`) work well for most prompts. + - Ensure that the reference clip matches the specified language tag. Otherwise, language transfer outputs may inherit the accent of the reference clip’s language. To mitigate this, set `cfg_weight` to `0`. + - The default settings (`exaggeration=0.5`, `cfg_weight=0.5`) work well for most prompts across all languages. - If the reference speaker has a fast speaking style, lowering `cfg_weight` to around `0.3` can improve pacing. - **Expressive or Dramatic Speech:** @@ -50,19 +55,31 @@ git clone https://github.com/resemble-ai/chatterbox.git cd chatterbox pip install -e . ``` -We developed and tested Chatterbox on Python 3.11 on Debain 11 OS; the versions of the dependencies are pinned in `pyproject.toml` to ensure consistency. You can modify the code or dependencies in this installation mode. - +We developed and tested Chatterbox on Python 3.11 on Debian 11 OS; the versions of the dependencies are pinned in `pyproject.toml` to ensure consistency. You can modify the code or dependencies in this installation mode. # Usage ```python import torchaudio as ta from chatterbox.tts import ChatterboxTTS + +# English example model = ChatterboxTTS.from_pretrained(device="cuda") text = "Ezreal and Jinx teamed up with Ahri, Yasuo, and Teemo to take down the enemy's Nexus in an epic late-game pentakill." wav = model.generate(text) -ta.save("test-1.wav", wav, model.sr) +ta.save("test-english.wav", wav, model.sr) + +# Multilingual examples +multilingual_model = ChatterboxMultilingualTTS.from_pretrained(device=device) + +french_text = "Bonjour, comment ça va? Ceci est le modèle de synthèse vocale multilingue Chatterbox, il prend en charge 23 langues." +wav_french = multilingual_model.generate(spanish_text, language_id="fr") +ta.save("test-french.wav", wav_french, model.sr) + +chinese_text = "你好，今天天气真不错，希望你有一个愉快的周末。" +wav_chinese = multilingual_model.generate(chinese_text, language_id="zh") +ta.save("test-chinese.wav", wav_chinese, model.sr) # If you want to synthesize with a different voice, specify the audio prompt AUDIO_PROMPT_PATH = "YOUR_FILE.wav" @@ -71,9 +88,6 @@ ta.save("test-2.wav", wav, model.sr) ``` See `example_tts.py` and `example_vc.py` for more examples. -# Supported Lanugage -Currenlty only English. - # Acknowledgements - [Cosyvoice](https://github.com/FunAudioLLM/CosyVoice) - [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) diff --git a/example_tts.py b/example_tts.py index 0efcf820..f827c13f 100644 --- a/example_tts.py +++ b/example_tts.py @@ -1,6 +1,7 @@ import torchaudio as ta import torch from chatterbox.tts import ChatterboxTTS +from chatterbox.mtl_tts import ChatterboxMultilingualTTS # Automatically detect the best available device if torch.cuda.is_available(): @@ -18,7 +19,13 @@ wav = model.generate(text) ta.save("test-1.wav", wav, model.sr) +multilingual_model = ChatterboxMultilingualTTS.from_pretrained(device=device) +text = "Bonjour, comment ça va? Ceci est le modèle de synthèse vocale multilingue Chatterbox, il prend en charge 23 langues." +wav = multilingual_model.generate(text, language_id="fr") +ta.save("test-2.wav", wav, multilingual_model.sr) + + # If you want to synthesize with a different voice, specify the audio prompt AUDIO_PROMPT_PATH = "YOUR_FILE.wav" wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH) -ta.save("test-2.wav", wav, model.sr) +ta.save("test-3.wav", wav, model.sr) diff --git a/multilingual_app.py b/multilingual_app.py new file mode 100644 index 00000000..f98b0e4f --- /dev/null +++ b/multilingual_app.py @@ -0,0 +1,319 @@ +import random +import numpy as np +import torch +from src.chatterbox.mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES +import gradio as gr +import spaces + +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +print(f"🚀 Running on device: {DEVICE}") + +# --- Global Model Initialization --- +MODEL = None + +LANGUAGE_CONFIG = { + "ar": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/ar_f/ar_prompts2.flac", + "text": "في الشهر الماضي، وصلنا إلى معلم جديد بمليارين من المشاهدات على قناتنا على يوتيوب." + }, + "da": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/da_m1.flac", + "text": "Sidste måned nåede vi en ny milepæl med to milliarder visninger på vores YouTube-kanal." + }, + "de": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/de_f1.flac", + "text": "Letzten Monat haben wir einen neuen Meilenstein erreicht: zwei Milliarden Aufrufe auf unserem YouTube-Kanal." + }, + "el": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/el_m.flac", + "text": "Τον περασμένο μήνα, φτάσαμε σε ένα νέο ορόσημο με δύο δισεκατομμύρια προβολές στο κανάλι μας στο YouTube." + }, + "en": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/en_f1.flac", + "text": "Last month, we reached a new milestone with two billion views on our YouTube channel." + }, + "es": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/es_f1.flac", + "text": "El mes pasado alcanzamos un nuevo hito: dos mil millones de visualizaciones en nuestro canal de YouTube." + }, + "fi": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/fi_m.flac", + "text": "Viime kuussa saavutimme uuden virstanpylvään kahden miljardin katselukerran kanssa YouTube-kanavallamme." + }, + "fr": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/fr_f1.flac", + "text": "Le mois dernier, nous avons atteint un nouveau jalon avec deux milliards de vues sur notre chaîne YouTube." + }, + "he": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/he_m1.flac", + "text": "בחודש שעבר הגענו לאבן דרך חדשה עם שני מיליארד צפיות בערוץ היוטיוב שלנו." + }, + "hi": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/hi_f1.flac", + "text": "पिछले महीने हमने एक नया मील का पत्थर छुआ: हमारे YouTube चैनल पर दो अरब व्यूज़।" + }, + "it": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/it_m1.flac", + "text": "Il mese scorso abbiamo raggiunto un nuovo traguardo: due miliardi di visualizzazioni sul nostro canale YouTube." + }, + "ja": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/ja/ja_prompts1.flac", + "text": "先月、私たちのYouTubeチャンネルで二十億回の再生回数という新たなマイルストーンに到達しました。" + }, + "ko": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/ko_f.flac", + "text": "지난달 우리는 유튜브 채널에서 이십억 조회수라는 새로운 이정표에 도달했습니다." + }, + "ms": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/ms_f.flac", + "text": "Bulan lepas, kami mencapai pencapaian baru dengan dua bilion tontonan di saluran YouTube kami." + }, + "nl": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/nl_m.flac", + "text": "Vorige maand bereikten we een nieuwe mijlpaal met twee miljard weergaven op ons YouTube-kanaal." + }, + "no": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/no_f1.flac", + "text": "Forrige måned nådde vi en ny milepæl med to milliarder visninger på YouTube-kanalen vår." + }, + "pl": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/pl_m.flac", + "text": "W zeszłym miesiącu osiągnęliśmy nowy kamień milowy z dwoma miliardami wyświetleń na naszym kanale YouTube." + }, + "pt": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/pt_m1.flac", + "text": "No mês passado, alcançámos um novo marco: dois mil milhões de visualizações no nosso canal do YouTube." + }, + "ru": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/ru_m.flac", + "text": "В прошлом месяце мы достигли нового рубежа: два миллиарда просмотров на нашем YouTube-канале." + }, + "sv": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/sv_f.flac", + "text": "Förra månaden nådde vi en ny milstolpe med två miljarder visningar på vår YouTube-kanal." + }, + "sw": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/sw_m.flac", + "text": "Mwezi uliopita, tulifika hatua mpya ya maoni ya bilioni mbili kweny kituo chetu cha YouTube." + }, + "tr": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/tr_m.flac", + "text": "Geçen ay YouTube kanalımızda iki milyar görüntüleme ile yeni bir dönüm noktasına ulaştık." + }, + "zh": { + "audio": "https://storage.googleapis.com/chatterbox-demo-samples/mtl_prompts/zh_f2.flac", + "text": "上个月，我们达到了一个新的里程碑. 我们的YouTube频道观看次数达到了二十亿次，这绝对令人难以置信。" + }, +} + +# --- UI Helpers --- +def default_audio_for_ui(lang: str) -> str | None: + return LANGUAGE_CONFIG.get(lang, {}).get("audio") + + +def default_text_for_ui(lang: str) -> str: + return LANGUAGE_CONFIG.get(lang, {}).get("text", "") + + +def get_supported_languages_display() -> str: + """Generate a formatted display of all supported languages.""" + language_items = [] + for code, name in sorted(SUPPORTED_LANGUAGES.items()): + language_items.append(f"**{name}** (`{code}`)") + + # Split into 2 lines + mid = len(language_items) // 2 + line1 = " • ".join(language_items[:mid]) + line2 = " • ".join(language_items[mid:]) + + return f""" +### 🌍 Supported Languages ({len(SUPPORTED_LANGUAGES)} total) +{line1} + +{line2} +""" + + +def get_or_load_model(): + """Loads the ChatterboxMultilingualTTS model if it hasn't been loaded already, + and ensures it's on the correct device.""" + global MODEL + if MODEL is None: + print("Model not loaded, initializing...") + try: + MODEL = ChatterboxMultilingualTTS.from_pretrained(DEVICE) + if hasattr(MODEL, 'to') and str(MODEL.device) != DEVICE: + MODEL.to(DEVICE) + print(f"Model loaded successfully. Internal device: {getattr(MODEL, 'device', 'N/A')}") + except Exception as e: + print(f"Error loading model: {e}") + raise + return MODEL + +# Attempt to load the model at startup. +try: + get_or_load_model() +except Exception as e: + print(f"CRITICAL: Failed to load model on startup. Application may not function. Error: {e}") + +def set_seed(seed: int): + """Sets the random seed for reproducibility across torch, numpy, and random.""" + torch.manual_seed(seed) + if DEVICE == "cuda": + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + random.seed(seed) + np.random.seed(seed) + +def resolve_audio_prompt(language_id: str, provided_path: str | None) -> str | None: + """ + Decide which audio prompt to use: + - If user provided a path (upload/mic/url), use it. + - Else, fall back to language-specific default (if any). + """ + if provided_path and str(provided_path).strip(): + return provided_path + return LANGUAGE_CONFIG.get(language_id, {}).get("audio") + + +@spaces.GPU +def generate_tts_audio( + text_input: str, + language_id: str, + audio_prompt_path_input: str = None, + exaggeration_input: float = 0.5, + temperature_input: float = 0.8, + seed_num_input: int = 0, + cfgw_input: float = 0.5 +) -> tuple[int, np.ndarray]: + """ + Generate high-quality speech audio from text using Chatterbox Multilingual model with optional reference audio styling. + Supported languages: English, French, German, Spanish, Italian, Portuguese, and Hindi. + + This tool synthesizes natural-sounding speech from input text. When a reference audio file + is provided, it captures the speaker's voice characteristics and speaking style. The generated audio + maintains the prosody, tone, and vocal qualities of the reference speaker, or uses default voice if no reference is provided. + + Args: + text_input (str): The text to synthesize into speech (maximum 300 characters) + language_id (str): The language code for synthesis (eg. en, fr, de, es, it, pt, hi) + audio_prompt_path_input (str, optional): File path or URL to the reference audio file that defines the target voice style. Defaults to None. + exaggeration_input (float, optional): Controls speech expressiveness (0.25-2.0, neutral=0.5, extreme values may be unstable). Defaults to 0.5. + temperature_input (float, optional): Controls randomness in generation (0.05-5.0, higher=more varied). Defaults to 0.8. + seed_num_input (int, optional): Random seed for reproducible results (0 for random generation). Defaults to 0. + cfgw_input (float, optional): CFG/Pace weight controlling generation guidance (0.2-1.0). Defaults to 0.5, 0 for language transfer. + + Returns: + tuple[int, np.ndarray]: A tuple containing the sample rate (int) and the generated audio waveform (numpy.ndarray) + """ + current_model = get_or_load_model() + + if current_model is None: + raise RuntimeError("TTS model is not loaded.") + + if seed_num_input != 0: + set_seed(int(seed_num_input)) + + print(f"Generating audio for text: '{text_input[:50]}...'") + + # Handle optional audio prompt + chosen_prompt = audio_prompt_path_input or default_audio_for_ui(language_id) + + generate_kwargs = { + "exaggeration": exaggeration_input, + "temperature": temperature_input, + "cfg_weight": cfgw_input, + } + if chosen_prompt: + generate_kwargs["audio_prompt_path"] = chosen_prompt + print(f"Using audio prompt: {chosen_prompt}") + else: + print("No audio prompt provided; using default voice.") + + wav = current_model.generate( + text_input[:300], # Truncate text to max chars + language_id=language_id, + **generate_kwargs + ) + print("Audio generation complete.") + return (current_model.sr, wav.squeeze(0).numpy()) + +with gr.Blocks() as demo: + gr.Markdown( + """ + # Chatterbox Multilingual Demo + Generate high-quality multilingual speech from text with reference audio styling, supporting 23 languages. + """ + ) + + # Display supported languages + gr.Markdown(get_supported_languages_display()) + with gr.Row(): + with gr.Column(): + initial_lang = "fr" + text = gr.Textbox( + value=default_text_for_ui(initial_lang), + label="Text to synthesize (max chars 300)", + max_lines=5 + ) + + language_id = gr.Dropdown( + choices=list(ChatterboxMultilingualTTS.get_supported_languages().keys()), + value=initial_lang, + label="Language", + info="Select the language for text-to-speech synthesis" + ) + + ref_wav = gr.Audio( + sources=["upload", "microphone"], + type="filepath", + label="Reference Audio File (Optional)", + value=default_audio_for_ui(initial_lang) + ) + + gr.Markdown( + "💡 **Note**: Ensure that the reference clip matches the specified language tag. Otherwise, language transfer outputs may inherit the accent of the reference clip's language. To mitigate this, set the CFG weight to 0.", + elem_classes=["audio-note"] + ) + + exaggeration = gr.Slider( + 0.25, 2, step=.05, label="Exaggeration (Neutral = 0.5, extreme values can be unstable)", value=.5 + ) + cfg_weight = gr.Slider( + 0.2, 1, step=.05, label="CFG/Pace", value=0.5 + ) + + with gr.Accordion("More options", open=False): + seed_num = gr.Number(value=0, label="Random seed (0 for random)") + temp = gr.Slider(0.05, 5, step=.05, label="Temperature", value=.8) + + run_btn = gr.Button("Generate", variant="primary") + + with gr.Column(): + audio_output = gr.Audio(label="Output Audio") + + def on_language_change(lang, current_ref, current_text): + return default_audio_for_ui(lang), default_text_for_ui(lang) + + language_id.change( + fn=on_language_change, + inputs=[language_id, ref_wav, text], + outputs=[ref_wav, text], + show_progress=False + ) + + run_btn.click( + fn=generate_tts_audio, + inputs=[ + text, + language_id, + ref_wav, + exaggeration, + temp, + seed_num, + cfg_weight, + ], + outputs=[audio_output], + ) + +demo.launch(mcp_server=True) diff --git a/pyproject.toml b/pyproject.toml index 9e1bbba4..e3e6ad7c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,9 @@ dependencies = [ "diffusers==0.29.0", "resemble-perth==1.0.1", "conformer==0.3.2", - "safetensors==0.5.3" + "safetensors==0.5.3", + "pkuseg ==0.0.25", + "pykakasi==2.3.0" ] [project.urls] diff --git a/src/chatterbox/__init__.py b/src/chatterbox/__init__.py index 4f5381fb..190cfbf2 100644 --- a/src/chatterbox/__init__.py +++ b/src/chatterbox/__init__.py @@ -8,3 +8,4 @@ from .tts import ChatterboxTTS from .vc import ChatterboxVC +from .mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES \ No newline at end of file diff --git a/src/chatterbox/models/s3gen/flow.py b/src/chatterbox/models/s3gen/flow.py index 8069f4e0..ad19cfa3 100644 --- a/src/chatterbox/models/s3gen/flow.py +++ b/src/chatterbox/models/s3gen/flow.py @@ -14,6 +14,8 @@ import logging import random from typing import Dict, Optional + +logger = logging.getLogger(__name__) import torch import torch.nn as nn from torch.nn import functional as F @@ -94,7 +96,7 @@ def forward( # concat text and prompt_text mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device) - token = self.input_embedding(torch.clamp(token, min=0)) * mask + token = self.input_embedding(torch.clamp(token, min=0, max=self.input_embedding.num_embeddings-1)) * mask # text encode h, h_lengths = self.encoder(token, token_len) @@ -144,7 +146,13 @@ def inference(self, token_len1, token_len2 = prompt_token.shape[1], token.shape[1] token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) - token = self.input_embedding(torch.clamp(token, min=0)) * mask + + # Check for out-of-bounds token IDs + vocab_size = self.input_embedding.num_embeddings + if token.max() >= vocab_size or token.min() < 0: + logging.warning(f"S3Gen: Token IDs out of bounds: min={token.min().item()}, max={token.max().item()}, vocab_size={vocab_size}") + + token = self.input_embedding(torch.clamp(token, min=0, max=vocab_size-1)) * mask # text encode h, h_lengths = self.encoder(token, token_len) @@ -255,7 +263,7 @@ def inference(self, # concat text and prompt_text token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding) - token = self.input_embedding(torch.clamp(token, min=0)) * mask + token = self.input_embedding(torch.clamp(token, min=0, max=self.input_embedding.num_embeddings-1)) * mask # text encode h, h_lengths = self.encoder(token, token_len) diff --git a/src/chatterbox/models/s3gen/utils/mel.py b/src/chatterbox/models/s3gen/utils/mel.py index 5a9ff9d1..907d2b57 100644 --- a/src/chatterbox/models/s3gen/utils/mel.py +++ b/src/chatterbox/models/s3gen/utils/mel.py @@ -1,8 +1,11 @@ """mel-spectrogram extraction in Matcha-TTS""" +import logging from librosa.filters import mel as librosa_mel_fn import torch import numpy as np +logger = logging.getLogger(__name__) + # NOTE: they decalred these global vars mel_basis = {} @@ -42,10 +45,11 @@ def mel_spectrogram(y, n_fft=1920, num_mels=80, sampling_rate=24000, hop_size=48 if len(y.shape) == 1: y = y[None, ] - if torch.min(y) < -1.0: - print("min value is ", torch.min(y)) - if torch.max(y) > 1.0: - print("max value is ", torch.max(y)) + # Debug: Check for audio clipping (values outside [-1.0, 1.0] range) + min_val = torch.min(y) + max_val = torch.max(y) + if min_val < -1.0 or max_val > 1.0: + logger.warning(f"Audio values outside normalized range: min={min_val.item():.4f}, max={max_val.item():.4f}") global mel_basis, hann_window # pylint: disable=global-statement,global-variable-not-assigned if f"{str(fmax)}_{str(y.device)}" not in mel_basis: diff --git a/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py b/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py index d3a144f0..255d50f0 100644 --- a/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py +++ b/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py @@ -10,6 +10,9 @@ logger = logging.getLogger(__name__) +LLAMA_ALIGNED_HEADS = [(12, 15), (13, 11), (9, 2)] + + @dataclass class AlignmentAnalysisResult: # was this frame detected as being part of a noisy beginning chunk with potential hallucinations? @@ -49,21 +52,22 @@ def __init__(self, tfmr, queue, text_tokens_slice, alignment_layer_idx=9, eos_id self.complete = False self.completed_at = None + + # Track generated tokens for repetition detection + self.generated_tokens = [] # Using `output_attentions=True` is incompatible with optimized attention kernels, so # using it for all layers slows things down too much. We can apply it to just one layer # by intercepting the kwargs and adding a forward hook (credit: jrm) - self.last_aligned_attn = None - self._add_attention_spy(tfmr, alignment_layer_idx) + self.last_aligned_attns = [] + for i, (layer_idx, head_idx) in enumerate(LLAMA_ALIGNED_HEADS): + self.last_aligned_attns += [None] + self._add_attention_spy(tfmr, i, layer_idx, head_idx) - def _add_attention_spy(self, tfmr, alignment_layer_idx): + def _add_attention_spy(self, tfmr, buffer_idx, layer_idx, head_idx): """ Adds a forward hook to a specific attention layer to collect outputs. - Using `output_attentions=True` is incompatible with optimized attention kernels, so - using it for all layers slows things down too much. - (credit: jrm) """ - def attention_forward_hook(module, input, output): """ See `LlamaAttention.forward`; the output is a 3-tuple: `attn_output, attn_weights, past_key_value`. @@ -71,27 +75,23 @@ def attention_forward_hook(module, input, output): - When `output_attentions=True`, `LlamaSdpaAttention.forward` calls `LlamaAttention.forward`. - `attn_output` has shape [B, H, T0, T0] for the 0th entry, and [B, H, 1, T0+i] for the rest i-th. """ - step_attention = output[1].cpu() # (B, 16, N, N) - self.last_aligned_attn = step_attention[0].mean(0) # (N, N) - - target_layer = tfmr.layers[alignment_layer_idx].self_attn - hook_handle = target_layer.register_forward_hook(attention_forward_hook) - - # Backup original forward - original_forward = target_layer.forward - def patched_forward(self, *args, **kwargs): - kwargs['output_attentions'] = True - return original_forward(*args, **kwargs) - - # TODO: how to unpatch it? - target_layer.forward = MethodType(patched_forward, target_layer) - - def step(self, logits): + if isinstance(output, tuple) and len(output) > 1 and output[1] is not None: + step_attention = output[1].cpu() # (B, n_heads, T0, Ti) + self.last_aligned_attns[buffer_idx] = step_attention[0, head_idx] # (T0, Ti) + + target_layer = tfmr.layers[layer_idx].self_attn + # Register hook and store the handle + target_layer.register_forward_hook(attention_forward_hook) + if hasattr(tfmr, 'config') and hasattr(tfmr.config, 'output_attentions'): + self.original_output_attentions = tfmr.config.output_attentions + tfmr.config.output_attentions = True + + def step(self, logits, next_token=None): """ Emits an AlignmentAnalysisResult into the output queue, and potentially modifies the logits to force an EOS. """ # extract approximate alignment matrix chunk (1 frame at a time after the first chunk) - aligned_attn = self.last_aligned_attn # (N, N) + aligned_attn = torch.stack(self.last_aligned_attns).mean(dim=0) # (N, N) i, j = self.text_tokens_slice if self.curr_frame_pos == 0: # first chunk has conditioning info, text tokens, and BOS token @@ -133,22 +133,46 @@ def step(self, logits): last_text_token_duration = A[15:, -3:].sum() # Activations for the final token that last too long are likely hallucinations. - long_tail = self.complete and (A[self.completed_at:, -3:].sum(dim=0).max() >= 10) # 400ms + long_tail = self.complete and (A[self.completed_at:, -3:].sum(dim=0).max() >= 5) # 200ms # If there are activations in previous tokens after generation has completed, assume this is a repetition error. - repetition = self.complete and (A[self.completed_at:, :-5].max(dim=1).values.sum() > 5) + alignment_repetition = self.complete and (A[self.completed_at:, :-5].max(dim=1).values.sum() > 5) + + # Track generated tokens for repetition detection + if next_token is not None: + # Convert tensor to scalar if needed + if isinstance(next_token, torch.Tensor): + token_id = next_token.item() if next_token.numel() == 1 else next_token.view(-1)[0].item() + else: + token_id = next_token + self.generated_tokens.append(token_id) + + # Keep only last 8 tokens to prevent memory issues + if len(self.generated_tokens) > 8: + self.generated_tokens = self.generated_tokens[-8:] + + # Check for excessive token repetition (3x same token in a row) + token_repetition = ( + # self.complete and + len(self.generated_tokens) >= 3 and + len(set(self.generated_tokens[-2:])) == 1 + ) + + if token_repetition: + repeated_token = self.generated_tokens[-1] + logger.warning(f"🚨 Detected 2x repetition of token {repeated_token}") + + # Suppress EoS to prevent early termination + if cur_text_posn < S - 3 and S > 5: # Only suppress if text is longer than 5 tokens + logits[..., self.eos_idx] = -2**15 # If a bad ending is detected, force emit EOS by modifying logits # NOTE: this means logits may be inconsistent with latents! - if long_tail or repetition: - logger.warn(f"forcing EOS token, {long_tail=}, {repetition=}") + if long_tail or alignment_repetition or token_repetition: + logger.warning(f"forcing EOS token, {long_tail=}, {alignment_repetition=}, {token_repetition=}") # (±2**15 is safe for all dtypes >= 16bit) logits = -(2**15) * torch.ones_like(logits) logits[..., self.eos_idx] = 2**15 - # Suppress EoS to prevent early termination - if cur_text_posn < S - 3: # FIXME: arbitrary - logits[..., self.eos_idx] = -2**15 - self.curr_frame_pos += 1 return logits diff --git a/src/chatterbox/models/t3/modules/t3_config.py b/src/chatterbox/models/t3/modules/t3_config.py index 2769d835..3e029de9 100644 --- a/src/chatterbox/models/t3/modules/t3_config.py +++ b/src/chatterbox/models/t3/modules/t3_config.py @@ -2,26 +2,40 @@ class T3Config: - start_text_token = 255 - stop_text_token = 0 - text_tokens_dict_size = 704 - max_text_tokens = 2048 + def __init__(self, text_tokens_dict_size=704): + self.start_text_token = 255 + self.stop_text_token = 0 + self.text_tokens_dict_size = text_tokens_dict_size + self.max_text_tokens = 2048 - start_speech_token = 6561 - stop_speech_token = 6562 - speech_tokens_dict_size = 8194 - max_speech_tokens = 4096 + self.start_speech_token = 6561 + self.stop_speech_token = 6562 + self.speech_tokens_dict_size = 8194 + self.max_speech_tokens = 4096 - llama_config_name = "Llama_520M" - input_pos_emb = "learned" - speech_cond_prompt_len = 150 + self.llama_config_name = "Llama_520M" + self.input_pos_emb = "learned" + self.speech_cond_prompt_len = 150 - # For T3CondEnc - encoder_type = "voice_encoder" - speaker_embed_size = 256 - use_perceiver_resampler = True - emotion_adv = True + self.encoder_type = "voice_encoder" + self.speaker_embed_size = 256 + self.use_perceiver_resampler = True + self.emotion_adv = True @property def n_channels(self): return LLAMA_CONFIGS[self.llama_config_name]["hidden_size"] + + @property + def is_multilingual(self): + return self.text_tokens_dict_size == 2352 + + @classmethod + def english_only(cls): + """Create configuration for English-only TTS model.""" + return cls(text_tokens_dict_size=704) + + @classmethod + def multilingual(cls): + """Create configuration for multilingual TTS model.""" + return cls(text_tokens_dict_size=2352) \ No newline at end of file diff --git a/src/chatterbox/models/t3/t3.py b/src/chatterbox/models/t3/t3.py index 4984470c..905566c6 100644 --- a/src/chatterbox/models/t3/t3.py +++ b/src/chatterbox/models/t3/t3.py @@ -3,12 +3,14 @@ import logging from typing import Union, Optional, List +logger = logging.getLogger(__name__) + from tqdm import tqdm import torch import torch.nn.functional as F from torch import nn, Tensor from transformers import LlamaModel, LlamaConfig -from transformers.generation.logits_process import MinPLogitsWarper, RepetitionPenaltyLogitsProcessor, TopPLogitsWarper +from transformers.generation.logits_process import TopPLogitsWarper, RepetitionPenaltyLogitsProcessor, MinPLogitsWarper from .modules.learned_pos_emb import LearnedPositionEmbeddings @@ -16,6 +18,7 @@ from .modules.t3_config import T3Config from .llama_configs import LLAMA_CONFIGS from .inference.t3_hf_backend import T3HuggingfaceBackend +from .inference.alignment_stream_analyzer import AlignmentStreamAnalyzer from ..utils import AttrDict @@ -38,7 +41,9 @@ class T3(nn.Module): different PE embedding space for speech. """ - def __init__(self, hp=T3Config()): + def __init__(self, hp=None): + if hp is None: + hp = T3Config.english_only() # Default to English-only config for backward compatibility super().__init__() self.hp = hp self.cfg = LlamaConfig(**LLAMA_CONFIGS[hp.llama_config_name]) @@ -217,11 +222,11 @@ def inference( stop_on_eos=True, do_sample=True, temperature=0.8, + top_p=0.95, min_p=0.05, - top_p=1.00, length_penalty=1.0, repetition_penalty=1.2, - cfg_weight=0, + cfg_weight=0.5, ): """ Args: @@ -252,12 +257,24 @@ def inference( # TODO? synchronize the expensive compile function # with self.compile_lock: if not self.compiled: + # Default to None for English models, only create for multilingual + alignment_stream_analyzer = None + if self.hp.is_multilingual: + alignment_stream_analyzer = AlignmentStreamAnalyzer( + self.tfmr, + None, + text_tokens_slice=(len_cond, len_cond + text_tokens.size(-1)), + alignment_layer_idx=9, # TODO: hparam or something? + eos_idx=self.hp.stop_speech_token, + ) + assert alignment_stream_analyzer.eos_idx == self.hp.stop_speech_token + patched_model = T3HuggingfaceBackend( config=self.cfg, llama=self.tfmr, speech_enc=self.speech_emb, speech_head=self.speech_head, - alignment_stream_analyzer=None, + alignment_stream_analyzer=alignment_stream_analyzer, ) self.patched_model = patched_model self.compiled = True @@ -288,17 +305,15 @@ def inference( # batch_size=2 for CFG bos_embed = torch.cat([bos_embed, bos_embed]) - # Combine condition and BOS token for the initial input if cfg_weight > 0 - if cfg_weight > 0: - inputs_embeds = torch.cat([embeds, bos_embed], dim=1) - else: - inputs_embeds = embeds + # Combine condition and BOS token for the initial input + inputs_embeds = torch.cat([embeds, bos_embed], dim=1) # Track generated token ids; start with the BOS token. generated_ids = bos_token.clone() predicted = [] # To store the predicted tokens # Instantiate the logits processors. + top_p_warper = TopPLogitsWarper(top_p=top_p) min_p_warper = MinPLogitsWarper(min_p=min_p) top_p_warper = TopPLogitsWarper(top_p=top_p) repetition_penalty_processor = RepetitionPenaltyLogitsProcessor(penalty=float(repetition_penalty)) @@ -317,24 +332,32 @@ def inference( # ---- Generation Loop using kv_cache ---- for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True): - logits = output.logits[:, -1, :] - - # CFG - if cfg_weight > 0.0: - logits_cond = logits[0:1] - logits_uncond = logits[1:2] - logits = logits_cond + cfg_weight * (logits_cond - logits_uncond) - - logits = logits.squeeze(1) - + logits_step = output.logits[:, -1, :] + # CFG combine → (1, V) + cond = logits_step[0:1, :] + uncond = logits_step[1:2, :] + cfg = torch.as_tensor(cfg_weight, device=cond.device, dtype=cond.dtype) + logits = cond + cfg * (cond - uncond) + + # Apply alignment stream analyzer integrity checks + if self.patched_model.alignment_stream_analyzer is not None: + if logits.dim() == 1: # guard in case something upstream squeezed + logits = logits.unsqueeze(0) # (1, V) + # Pass the last generated token for repetition tracking + last_token = generated_ids[0, -1].item() if len(generated_ids[0]) > 0 else None + logits = self.patched_model.alignment_stream_analyzer.step(logits, next_token=last_token) # (1, V) + + # Apply repetition penalty + ids_for_proc = generated_ids[:1, ...] # batch = 1 + logits = repetition_penalty_processor(ids_for_proc, logits) # expects (B,V) + # Apply temperature scaling. if temperature != 1.0: logits = logits / temperature - - # Apply repetition penalty and top‑p filtering. - logits = repetition_penalty_processor(generated_ids, logits) - logits = min_p_warper(None, logits) - logits = top_p_warper(None, logits) + + # Apply min_p and top_p filtering + logits = min_p_warper(ids_for_proc, logits) + logits = top_p_warper(ids_for_proc, logits) # Convert logits to probabilities and sample the next token. probs = torch.softmax(logits, dim=-1) @@ -345,6 +368,7 @@ def inference( # Check for EOS token. if next_token.view(-1) == self.hp.stop_speech_token: + logger.info(f"✅ EOS token detected! Stopping generation at step {i+1}") break # Get embedding for the new token. @@ -352,8 +376,7 @@ def inference( next_token_embed = next_token_embed + self.speech_pos_emb.get_fixed_embedding(i + 1) # For CFG - if cfg_weight > 0.0: - next_token_embed = torch.cat([next_token_embed, next_token_embed]) + next_token_embed = torch.cat([next_token_embed, next_token_embed]) # Forward pass with only the new token and the cached past. output = self.patched_model( diff --git a/src/chatterbox/models/tokenizers/__init__.py b/src/chatterbox/models/tokenizers/__init__.py index 97457e6f..fdf6d727 100644 --- a/src/chatterbox/models/tokenizers/__init__.py +++ b/src/chatterbox/models/tokenizers/__init__.py @@ -1 +1 @@ -from .tokenizer import EnTokenizer +from .tokenizer import EnTokenizer, MTLTokenizer \ No newline at end of file diff --git a/src/chatterbox/models/tokenizers/tokenizer.py b/src/chatterbox/models/tokenizers/tokenizer.py index f3536bc2..a86a85b4 100644 --- a/src/chatterbox/models/tokenizers/tokenizer.py +++ b/src/chatterbox/models/tokenizers/tokenizer.py @@ -1,7 +1,12 @@ import logging +import json +import re import torch +from pathlib import Path +from unicodedata import category from tokenizers import Tokenizer +from huggingface_hub import hf_hub_download # Special tokens @@ -48,3 +53,227 @@ def decode(self, seq): txt = txt.replace(EOT, '') txt = txt.replace(UNK, '') return txt + + +# Model repository +REPO_ID = "ResembleAI/chatterbox" + +# Global instances for optional dependencies +_kakasi = None +_dicta = None + + +def is_kanji(c: str) -> bool: + """Check if character is kanji.""" + return 19968 <= ord(c) <= 40959 + + +def is_katakana(c: str) -> bool: + """Check if character is katakana.""" + return 12449 <= ord(c) <= 12538 + + +def hiragana_normalize(text: str) -> str: + """Japanese text normalization: converts kanji to hiragana; katakana remains the same.""" + global _kakasi + + try: + if _kakasi is None: + import pykakasi + _kakasi = pykakasi.kakasi() + + result = _kakasi.convert(text) + out = [] + + for r in result: + inp = r['orig'] + hira = r["hira"] + + # Any kanji in the phrase + if any([is_kanji(c) for c in inp]): + if hira and hira[0] in ["は", "へ"]: # Safety check for empty hira + hira = " " + hira + out.append(hira) + + # All katakana + elif all([is_katakana(c) for c in inp]) if inp else False: # Safety check for empty inp + out.append(r['orig']) + + else: + out.append(inp) + + normalized_text = "".join(out) + + # Decompose Japanese characters for tokenizer compatibility + import unicodedata + normalized_text = unicodedata.normalize('NFKD', normalized_text) + + return normalized_text + + except ImportError: + logger.warning("pykakasi not available - Japanese text processing skipped") + return text + + +def add_hebrew_diacritics(text: str) -> str: + """Hebrew text normalization: adds diacritics to Hebrew text.""" + global _dicta + + try: + if _dicta is None: + from dicta_onnx import Dicta + _dicta = Dicta() + + return _dicta.add_diacritics(text) + + except ImportError: + logger.warning("dicta_onnx not available - Hebrew text processing skipped") + return text + except Exception as e: + logger.warning(f"Hebrew diacritization failed: {e}") + return text + + +def korean_normalize(text: str) -> str: + """Korean text normalization: decompose syllables into Jamo for tokenization.""" + + def decompose_hangul(char): + """Decompose Korean syllable into Jamo components.""" + if not ('\uac00' <= char <= '\ud7af'): + return char + + # Hangul decomposition formula + base = ord(char) - 0xAC00 + initial = chr(0x1100 + base // (21 * 28)) + medial = chr(0x1161 + (base % (21 * 28)) // 28) + final = chr(0x11A7 + base % 28) if base % 28 > 0 else '' + + return initial + medial + final + + # Decompose syllables and normalize punctuation + result = ''.join(decompose_hangul(char) for char in text) + return result.strip() + + +class ChineseCangjieConverter: + """Converts Chinese characters to Cangjie codes for tokenization.""" + + def __init__(self, model_dir=None): + self.word2cj = {} + self.cj2word = {} + self.segmenter = None + self._load_cangjie_mapping(model_dir) + self._init_segmenter() + + def _load_cangjie_mapping(self, model_dir=None): + """Load Cangjie mapping from HuggingFace model repository.""" + try: + cangjie_file = hf_hub_download( + repo_id=REPO_ID, + filename="Cangjie5_TC.json", + cache_dir=model_dir + ) + + with open(cangjie_file, "r", encoding="utf-8") as fp: + data = json.load(fp) + + for entry in data: + word, code = entry.split("\t")[:2] + self.word2cj[word] = code + if code not in self.cj2word: + self.cj2word[code] = [word] + else: + self.cj2word[code].append(word) + + except Exception as e: + logger.warning(f"Could not load Cangjie mapping: {e}") + + def _init_segmenter(self): + """Initialize pkuseg segmenter.""" + try: + from pkuseg import pkuseg + self.segmenter = pkuseg() + except ImportError: + logger.warning("pkuseg not available - Chinese segmentation will be skipped") + self.segmenter = None + + def _cangjie_encode(self, glyph: str): + """Encode a single Chinese glyph to Cangjie code.""" + normed_glyph = glyph + code = self.word2cj.get(normed_glyph, None) + if code is None: # e.g. Japanese hiragana + return None + index = self.cj2word[code].index(normed_glyph) + index = str(index) if index > 0 else "" + return code + str(index) + + + + def __call__(self, text): + """Convert Chinese characters in text to Cangjie tokens.""" + output = [] + if self.segmenter is not None: + segmented_words = self.segmenter.cut(text) + full_text = " ".join(segmented_words) + else: + full_text = text + + for t in full_text: + if category(t) == "Lo": + cangjie = self._cangjie_encode(t) + if cangjie is None: + output.append(t) + continue + code = [] + for c in cangjie: + code.append(f"[cj_{c}]") + code.append("[cj_.]") + code = "".join(code) + output.append(code) + else: + output.append(t) + return "".join(output) + + +class MTLTokenizer: + def __init__(self, vocab_file_path): + self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path) + model_dir = Path(vocab_file_path).parent + self.cangjie_converter = ChineseCangjieConverter(model_dir) + self.check_vocabset_sot_eot() + + def check_vocabset_sot_eot(self): + voc = self.tokenizer.get_vocab() + assert SOT in voc + assert EOT in voc + + def text_to_tokens(self, text: str, language_id: str = None): + text_tokens = self.encode(text, language_id=language_id) + text_tokens = torch.IntTensor(text_tokens).unsqueeze(0) + return text_tokens + + def encode(self, txt: str, language_id: str = None): + # Language-specific text processing + if language_id == 'zh': + txt = self.cangjie_converter(txt) + elif language_id == 'ja': + txt = hiragana_normalize(txt) + elif language_id == 'he': + txt = add_hebrew_diacritics(txt) + elif language_id == 'ko': + txt = korean_normalize(txt) + + # Prepend language token + if language_id: + txt = f"[{language_id.lower()}]{txt}" + + txt = txt.replace(' ', SPACE) + return self.tokenizer.encode(txt).ids + + def decode(self, seq): + if isinstance(seq, torch.Tensor): + seq = seq.cpu().numpy() + + txt = self.tokenizer.decode(seq, skip_special_tokens=False) + txt = txt.replace(' ', '').replace(SPACE, ' ').replace(EOT, '').replace(UNK, '') + return txt diff --git a/src/chatterbox/mtl_tts.py b/src/chatterbox/mtl_tts.py new file mode 100644 index 00000000..fccfbf3d --- /dev/null +++ b/src/chatterbox/mtl_tts.py @@ -0,0 +1,301 @@ +from dataclasses import dataclass +from pathlib import Path +import os + +import librosa +import torch +import perth +import torch.nn.functional as F +from safetensors.torch import load_file as load_safetensors +from huggingface_hub import snapshot_download + +from .models.t3 import T3 +from .models.t3.modules.t3_config import T3Config +from .models.s3tokenizer import S3_SR, drop_invalid_tokens +from .models.s3gen import S3GEN_SR, S3Gen +from .models.tokenizers import MTLTokenizer +from .models.voice_encoder import VoiceEncoder +from .models.t3.modules.cond_enc import T3Cond + + +REPO_ID = "ResembleAI/chatterbox" + +# Supported languages for the multilingual model +SUPPORTED_LANGUAGES = { + "ar": "Arabic", + "da": "Danish", + "de": "German", + "el": "Greek", + "en": "English", + "es": "Spanish", + "fi": "Finnish", + "fr": "French", + "he": "Hebrew", + "hi": "Hindi", + "it": "Italian", + "ja": "Japanese", + "ko": "Korean", + "ms": "Malay", + "nl": "Dutch", + "no": "Norwegian", + "pl": "Polish", + "pt": "Portuguese", + "ru": "Russian", + "sv": "Swedish", + "sw": "Swahili", + "tr": "Turkish", + "zh": "Chinese", +} + + +def punc_norm(text: str) -> str: + """ + Quick cleanup func for punctuation from LLMs or + containing chars not seen often in the dataset + """ + if len(text) == 0: + return "You need to add some text for me to talk." + + # Capitalise first letter + if text[0].islower(): + text = text[0].upper() + text[1:] + + # Remove multiple space chars + text = " ".join(text.split()) + + # Replace uncommon/llm punc + punc_to_replace = [ + ("...", ", "), + ("…", ", "), + (":", ","), + (" - ", ", "), + (";", ", "), + ("—", "-"), + ("–", "-"), + (" ,", ","), + ("“", "\""), + ("”", "\""), + ("‘", "'"), + ("’", "'"), + ] + for old_char_sequence, new_char in punc_to_replace: + text = text.replace(old_char_sequence, new_char) + + # Add full stop if no ending punc + text = text.rstrip(" ") + sentence_enders = {".", "!", "?", "-", ",","、","，","。","？","！"} + if not any(text.endswith(p) for p in sentence_enders): + text += "." + + return text + + +@dataclass +class Conditionals: + """ + Conditionals for T3 and S3Gen + - T3 conditionals: + - speaker_emb + - clap_emb + - cond_prompt_speech_tokens + - cond_prompt_speech_emb + - emotion_adv + - S3Gen conditionals: + - prompt_token + - prompt_token_len + - prompt_feat + - prompt_feat_len + - embedding + """ + t3: T3Cond + gen: dict + + def to(self, device): + self.t3 = self.t3.to(device=device) + for k, v in self.gen.items(): + if torch.is_tensor(v): + self.gen[k] = v.to(device=device) + return self + + def save(self, fpath: Path): + arg_dict = dict( + t3=self.t3.__dict__, + gen=self.gen + ) + torch.save(arg_dict, fpath) + + @classmethod + def load(cls, fpath, map_location="cpu"): + kwargs = torch.load(fpath, map_location=map_location, weights_only=True) + return cls(T3Cond(**kwargs['t3']), kwargs['gen']) + + +class ChatterboxMultilingualTTS: + ENC_COND_LEN = 6 * S3_SR + DEC_COND_LEN = 10 * S3GEN_SR + + def __init__( + self, + t3: T3, + s3gen: S3Gen, + ve: VoiceEncoder, + tokenizer: MTLTokenizer, + device: str, + conds: Conditionals = None, + ): + self.sr = S3GEN_SR # sample rate of synthesized audio + self.t3 = t3 + self.s3gen = s3gen + self.ve = ve + self.tokenizer = tokenizer + self.device = device + self.conds = conds + self.watermarker = perth.PerthImplicitWatermarker() + + @classmethod + def get_supported_languages(cls): + """Return dictionary of supported language codes and names.""" + return SUPPORTED_LANGUAGES.copy() + + @classmethod + def from_local(cls, ckpt_dir, device) -> 'ChatterboxMultilingualTTS': + ckpt_dir = Path(ckpt_dir) + + ve = VoiceEncoder() + ve.load_state_dict( + torch.load(ckpt_dir / "ve.pt", weights_only=True) + ) + ve.to(device).eval() + + t3 = T3(T3Config.multilingual()) + t3_state = load_safetensors(ckpt_dir / "t3_23lang.safetensors") + if "model" in t3_state.keys(): + t3_state = t3_state["model"][0] + t3.load_state_dict(t3_state) + t3.to(device).eval() + + s3gen = S3Gen() + s3gen.load_state_dict( + torch.load(ckpt_dir / "s3gen.pt", weights_only=True) + ) + s3gen.to(device).eval() + + tokenizer = MTLTokenizer( + str(ckpt_dir / "mtl_tokenizer.json") + ) + + conds = None + if (builtin_voice := ckpt_dir / "conds.pt").exists(): + conds = Conditionals.load(builtin_voice).to(device) + + return cls(t3, s3gen, ve, tokenizer, device, conds=conds) + + @classmethod + def from_pretrained(cls, device: torch.device) -> 'ChatterboxMultilingualTTS': + ckpt_dir = Path( + snapshot_download( + repo_id=REPO_ID, + repo_type="model", + revision="main", + allow_patterns=["ve.pt", "t3_23lang.safetensors", "s3gen.pt", "mtl_tokenizer.json", "conds.pt", "Cangjie5_TC.json"], + token=os.getenv("HF_TOKEN"), + ) + ) + return cls.from_local(ckpt_dir, device) + + def prepare_conditionals(self, wav_fpath, exaggeration=0.5): + ## Load reference wav + s3gen_ref_wav, _sr = librosa.load(wav_fpath, sr=S3GEN_SR) + + ref_16k_wav = librosa.resample(s3gen_ref_wav, orig_sr=S3GEN_SR, target_sr=S3_SR) + + s3gen_ref_wav = s3gen_ref_wav[:self.DEC_COND_LEN] + s3gen_ref_dict = self.s3gen.embed_ref(s3gen_ref_wav, S3GEN_SR, device=self.device) + + # Speech cond prompt tokens + t3_cond_prompt_tokens = None + if plen := self.t3.hp.speech_cond_prompt_len: + s3_tokzr = self.s3gen.tokenizer + t3_cond_prompt_tokens, _ = s3_tokzr.forward([ref_16k_wav[:self.ENC_COND_LEN]], max_len=plen) + t3_cond_prompt_tokens = torch.atleast_2d(t3_cond_prompt_tokens).to(self.device) + + # Voice-encoder speaker embedding + ve_embed = torch.from_numpy(self.ve.embeds_from_wavs([ref_16k_wav], sample_rate=S3_SR)) + ve_embed = ve_embed.mean(axis=0, keepdim=True).to(self.device) + + t3_cond = T3Cond( + speaker_emb=ve_embed, + cond_prompt_speech_tokens=t3_cond_prompt_tokens, + emotion_adv=exaggeration * torch.ones(1, 1, 1), + ).to(device=self.device) + self.conds = Conditionals(t3_cond, s3gen_ref_dict) + + def generate( + self, + text, + language_id, + audio_prompt_path=None, + exaggeration=0.5, + cfg_weight=0.5, + temperature=0.8, + repetition_penalty=2.0, + min_p=0.05, + top_p=1.0, + ): + # Validate language_id + if language_id and language_id.lower() not in SUPPORTED_LANGUAGES: + supported_langs = ", ".join(SUPPORTED_LANGUAGES.keys()) + raise ValueError( + f"Unsupported language_id '{language_id}'. " + f"Supported languages: {supported_langs}" + ) + + if audio_prompt_path: + self.prepare_conditionals(audio_prompt_path, exaggeration=exaggeration) + else: + assert self.conds is not None, "Please `prepare_conditionals` first or specify `audio_prompt_path`" + + # Update exaggeration if needed + if float(exaggeration) != float(self.conds.t3.emotion_adv[0, 0, 0].item()): + _cond: T3Cond = self.conds.t3 + self.conds.t3 = T3Cond( + speaker_emb=_cond.speaker_emb, + cond_prompt_speech_tokens=_cond.cond_prompt_speech_tokens, + emotion_adv=exaggeration * torch.ones(1, 1, 1), + ).to(device=self.device) + + # Norm and tokenize text + text = punc_norm(text) + text_tokens = self.tokenizer.text_to_tokens(text, language_id=language_id.lower() if language_id else None).to(self.device) + text_tokens = torch.cat([text_tokens, text_tokens], dim=0) # Need two seqs for CFG + + sot = self.t3.hp.start_text_token + eot = self.t3.hp.stop_text_token + text_tokens = F.pad(text_tokens, (1, 0), value=sot) + text_tokens = F.pad(text_tokens, (0, 1), value=eot) + + with torch.inference_mode(): + speech_tokens = self.t3.inference( + t3_cond=self.conds.t3, + text_tokens=text_tokens, + max_new_tokens=1000, # TODO: use the value in config + temperature=temperature, + cfg_weight=cfg_weight, + repetition_penalty=repetition_penalty, + min_p=min_p, + top_p=top_p, + ) + # Extract only the conditional batch. + speech_tokens = speech_tokens[0] + + # TODO: output becomes 1D + speech_tokens = drop_invalid_tokens(speech_tokens) + speech_tokens = speech_tokens.to(self.device) + + wav, _ = self.s3gen.inference( + speech_tokens=speech_tokens, + ref_dict=self.conds.gen, + ) + wav = wav.squeeze(0).detach().cpu().numpy() + watermarked_wav = self.watermarker.apply_watermark(wav, sample_rate=self.sr) + return torch.from_numpy(watermarked_wav).unsqueeze(0) From 214fbd197e3431cbd72c67dbdff21dcda85c984c Mon Sep 17 00:00:00 2001 From: manmay nakhashi Date: Thu, 4 Sep 2025 20:41:00 +0530 Subject: [PATCH 15/31] bump the version to 0.1.3 (#230) --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e3e6ad7c..2a089aca 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "chatterbox-tts" -version = "0.1.2" +version = "0.1.3" description = "Chatterbox: Open Source TTS and Voice Conversion by Resemble AI" readme = "README.md" requires-python = ">=3.9" From a8d8051eb7890eb17c2765efade58bc043ebc8ea Mon Sep 17 00:00:00 2001 From: Tedi Papajorgji Date: Thu, 4 Sep 2025 09:55:51 -0700 Subject: [PATCH 16/31] Update gradio links (#232) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a4ca2c74..29436c57 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ _Made with ♥️ by link). It delivers reliable performance with ultra-low latency of sub 200ms—ideal for production use in agents, applications, or interactive media. From 1798729e3ace636430d4c1ae0200a7d2859460b3 Mon Sep 17 00:00:00 2001 From: manmay nakhashi Date: Thu, 4 Sep 2025 23:08:53 +0530 Subject: [PATCH 17/31] remove src (#234) * remove src * bump version 0.1.4 * fix pip --- multilingual_app.py | 4 +--- pyproject.toml | 10 ++++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/multilingual_app.py b/multilingual_app.py index f98b0e4f..51e9c693 100644 --- a/multilingual_app.py +++ b/multilingual_app.py @@ -1,9 +1,8 @@ import random import numpy as np import torch -from src.chatterbox.mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES +from chatterbox.mtl_tts import ChatterboxMultilingualTTS, SUPPORTED_LANGUAGES import gradio as gr -import spaces DEVICE = "cuda" if torch.cuda.is_available() else "cpu" print(f"🚀 Running on device: {DEVICE}") @@ -176,7 +175,6 @@ def resolve_audio_prompt(language_id: str, provided_path: str | None) -> str | N return LANGUAGE_CONFIG.get(language_id, {}).get("audio") -@spaces.GPU def generate_tts_audio( text_input: str, language_id: str, diff --git a/pyproject.toml b/pyproject.toml index 2a089aca..e190b3af 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,15 +1,15 @@ [project] name = "chatterbox-tts" -version = "0.1.3" +version = "0.1.4" description = "Chatterbox: Open Source TTS and Voice Conversion by Resemble AI" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" license = {file = "LICENSE"} authors = [ {name = "resemble-ai", email = "engineering@resemble.ai"} ] dependencies = [ - "numpy>=1.26.0", + "numpy>=1.24.0,<1.26.0", "librosa==0.11.0", "s3tokenizer", "torch==2.6.0", @@ -20,7 +20,9 @@ dependencies = [ "conformer==0.3.2", "safetensors==0.5.3", "pkuseg ==0.0.25", - "pykakasi==2.3.0" + "pykakasi==2.3.0", + "gradio==5.44.1", + ] [project.urls] From a0434ffb3dbf4d3ed268580237ee991e06c8f9e8 Mon Sep 17 00:00:00 2001 From: zihanjin428 Date: Thu, 4 Sep 2025 14:11:56 -0400 Subject: [PATCH 18/31] Update README.md (#235) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 29436c57..79b9108e 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ We developed and tested Chatterbox on Python 3.11 on Debian 11 OS; the versions ```python import torchaudio as ta from chatterbox.tts import ChatterboxTTS - +from chatterbox.mtl_tts import ChatterboxMultilingualTTS # English example model = ChatterboxTTS.from_pretrained(device="cuda") From 1b5ae50585c8fa4d30ca6a7b304d787b4f802a42 Mon Sep 17 00:00:00 2001 From: Zohaib Ahmed Date: Thu, 4 Sep 2025 14:53:17 -0700 Subject: [PATCH 19/31] Update README.md with fixed image link (#236) --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 79b9108e..d6651ef7 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,5 @@ -Chatterbox Multilingual - +Chatterbox-Multilingual # Chatterbox TTS From bf169fe5f518760cb0b6c6a6eba3f885e10fa86f Mon Sep 17 00:00:00 2001 From: zihanjin428 Date: Thu, 25 Sep 2025 14:04:48 +0900 Subject: [PATCH 20/31] Multilingual v2 update (#295) * multilingual v2 vocab and russian stresser update * multilinugal tokenizer fix --- pyproject.toml | 3 +- src/chatterbox/models/t3/modules/t3_config.py | 4 +- src/chatterbox/models/tokenizers/tokenizer.py | 52 +++++++++++++++---- src/chatterbox/mtl_tts.py | 6 +-- 4 files changed, 50 insertions(+), 15 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e190b3af..780ec361 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,9 +19,10 @@ dependencies = [ "resemble-perth==1.0.1", "conformer==0.3.2", "safetensors==0.5.3", - "pkuseg ==0.0.25", + "spacy-pkuseg", "pykakasi==2.3.0", "gradio==5.44.1", + "russian-text-stresser @ git+https://github.com/Vuizur/add-stress-to-epub", ] diff --git a/src/chatterbox/models/t3/modules/t3_config.py b/src/chatterbox/models/t3/modules/t3_config.py index 3e029de9..55b43903 100644 --- a/src/chatterbox/models/t3/modules/t3_config.py +++ b/src/chatterbox/models/t3/modules/t3_config.py @@ -28,7 +28,7 @@ def n_channels(self): @property def is_multilingual(self): - return self.text_tokens_dict_size == 2352 + return self.text_tokens_dict_size == 2454 @classmethod def english_only(cls): @@ -38,4 +38,4 @@ def english_only(cls): @classmethod def multilingual(cls): """Create configuration for multilingual TTS model.""" - return cls(text_tokens_dict_size=2352) \ No newline at end of file + return cls(text_tokens_dict_size=2454) \ No newline at end of file diff --git a/src/chatterbox/models/tokenizers/tokenizer.py b/src/chatterbox/models/tokenizers/tokenizer.py index a86a85b4..84d45d35 100644 --- a/src/chatterbox/models/tokenizers/tokenizer.py +++ b/src/chatterbox/models/tokenizers/tokenizer.py @@ -1,10 +1,9 @@ import logging import json -import re import torch from pathlib import Path -from unicodedata import category +from unicodedata import category, normalize from tokenizers import Tokenizer from huggingface_hub import hf_hub_download @@ -33,7 +32,7 @@ def text_to_tokens(self, text: str): text_tokens = torch.IntTensor(text_tokens).unsqueeze(0) return text_tokens - def encode( self, txt: str, verbose=False): + def encode(self, txt: str): """ clean_text > (append `lang_id`) > replace SPACE > encode text using Tokenizer """ @@ -46,8 +45,7 @@ def decode(self, seq): if isinstance(seq, torch.Tensor): seq = seq.cpu().numpy() - txt: str = self.tokenizer.decode(seq, - skip_special_tokens=False) + txt: str = self.tokenizer.decode(seq, skip_special_tokens=False) txt = txt.replace(' ', '') txt = txt.replace(SPACE, ' ') txt = txt.replace(EOT, '') @@ -61,6 +59,7 @@ def decode(self, seq): # Global instances for optional dependencies _kakasi = None _dicta = None +_russian_stresser = None def is_kanji(c: str) -> bool: @@ -191,7 +190,7 @@ def _load_cangjie_mapping(self, model_dir=None): def _init_segmenter(self): """Initialize pkuseg segmenter.""" try: - from pkuseg import pkuseg + from spacy_pkuseg import pkuseg self.segmenter = pkuseg() except ImportError: logger.warning("pkuseg not available - Chinese segmentation will be skipped") @@ -235,6 +234,25 @@ def __call__(self, text): return "".join(output) +def add_russian_stress(text: str) -> str: + """Russian text normalization: adds stress marks to Russian text.""" + global _russian_stresser + + try: + if _russian_stresser is None: + from russian_text_stresser.text_stresser import RussianTextStresser + _russian_stresser = RussianTextStresser() + + return _russian_stresser.stress_text(text) + + except ImportError: + logger.warning("russian_text_stresser not available - Russian stress labeling skipped") + return text + except Exception as e: + logger.warning(f"Russian stress labeling failed: {e}") + return text + + class MTLTokenizer: def __init__(self, vocab_file_path): self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path) @@ -247,12 +265,26 @@ def check_vocabset_sot_eot(self): assert SOT in voc assert EOT in voc - def text_to_tokens(self, text: str, language_id: str = None): - text_tokens = self.encode(text, language_id=language_id) + def preprocess_text(self, raw_text: str, language_id: str = None, lowercase: bool = True, nfkd_normalize: bool = True): + """ + Text preprocessor that handles lowercase conversion and NFKD normalization. + """ + preprocessed_text = raw_text + if lowercase: + preprocessed_text = preprocessed_text.lower() + if nfkd_normalize: + preprocessed_text = normalize("NFKD", preprocessed_text) + + return preprocessed_text + + def text_to_tokens(self, text: str, language_id: str = None, lowercase: bool = True, nfkd_normalize: bool = True): + text_tokens = self.encode(text, language_id=language_id, lowercase=lowercase, nfkd_normalize=nfkd_normalize) text_tokens = torch.IntTensor(text_tokens).unsqueeze(0) return text_tokens - def encode(self, txt: str, language_id: str = None): + def encode(self, txt: str, language_id: str = None, lowercase: bool = True, nfkd_normalize: bool = True): + txt = self.preprocess_text(txt, language_id=language_id, lowercase=lowercase, nfkd_normalize=nfkd_normalize) + # Language-specific text processing if language_id == 'zh': txt = self.cangjie_converter(txt) @@ -262,6 +294,8 @@ def encode(self, txt: str, language_id: str = None): txt = add_hebrew_diacritics(txt) elif language_id == 'ko': txt = korean_normalize(txt) + elif language_id == 'ru': + txt = add_russian_stress(txt) # Prepend language token if language_id: diff --git a/src/chatterbox/mtl_tts.py b/src/chatterbox/mtl_tts.py index fccfbf3d..2c9cf052 100644 --- a/src/chatterbox/mtl_tts.py +++ b/src/chatterbox/mtl_tts.py @@ -168,7 +168,7 @@ def from_local(cls, ckpt_dir, device) -> 'ChatterboxMultilingualTTS': ve.to(device).eval() t3 = T3(T3Config.multilingual()) - t3_state = load_safetensors(ckpt_dir / "t3_23lang.safetensors") + t3_state = load_safetensors(ckpt_dir / "t3_mtl23ls_v2.safetensors") if "model" in t3_state.keys(): t3_state = t3_state["model"][0] t3.load_state_dict(t3_state) @@ -181,7 +181,7 @@ def from_local(cls, ckpt_dir, device) -> 'ChatterboxMultilingualTTS': s3gen.to(device).eval() tokenizer = MTLTokenizer( - str(ckpt_dir / "mtl_tokenizer.json") + str(ckpt_dir / "grapheme_mtl_merged_expanded_v1.json") ) conds = None @@ -197,7 +197,7 @@ def from_pretrained(cls, device: torch.device) -> 'ChatterboxMultilingualTTS': repo_id=REPO_ID, repo_type="model", revision="main", - allow_patterns=["ve.pt", "t3_23lang.safetensors", "s3gen.pt", "mtl_tokenizer.json", "conds.pt", "Cangjie5_TC.json"], + allow_patterns=["ve.pt", "t3_mtl23ls_v2.safetensors", "s3gen.pt", "grapheme_mtl_merged_expanded_v1.json", "conds.pt", "Cangjie5_TC.json"], token=os.getenv("HF_TOKEN"), ) ) From 84b4c1e66634448adf873941b061318179708930 Mon Sep 17 00:00:00 2001 From: "DESKTOP-OCNK43A\\Igor" Date: Mon, 20 Oct 2025 07:31:18 +0300 Subject: [PATCH 21/31] Update tts.py --- src/chatterbox/tts.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index d11fb527..bbf85aa1 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -1,3 +1,5 @@ +# chatterbox/tts.py + from dataclasses import dataclass from pathlib import Path From a9e8a884271026488c5a9111f68d936588ff2eb9 Mon Sep 17 00:00:00 2001 From: "DESKTOP-OCNK43A\\Igor" Date: Mon, 20 Oct 2025 14:43:12 +0300 Subject: [PATCH 22/31] added visual studio project --- chatterbox.pyproj | 35 +++++++++++++++++ run_tts_test.py | 99 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 134 insertions(+) create mode 100644 chatterbox.pyproj create mode 100644 run_tts_test.py diff --git a/chatterbox.pyproj b/chatterbox.pyproj new file mode 100644 index 00000000..80ff3e71 --- /dev/null +++ b/chatterbox.pyproj @@ -0,0 +1,35 @@ + + + Debug + 2.0 + 958ac2fa-ebae-40f7-bf36-e239ba9b0efd + . + chatterbox.py + + + . + . + chatterbox + chatterbox + + + true + false + + + true + false + + + + + + + + + + + + \ No newline at end of file diff --git a/run_tts_test.py b/run_tts_test.py new file mode 100644 index 00000000..0f3c8813 --- /dev/null +++ b/run_tts_test.py @@ -0,0 +1,99 @@ +# chatterbox/run_tts_test.py + +import torchaudio +import torch +from pathlib import Path +import time +import sys + +# --- Robust Path Setup --- +# Add the project root to the Python path to allow importing the chatterbox module +try: + # Assuming this script is in the 'chatterbox' directory, the project root is one level up. + project_root = Path(__file__).resolve().parent.parent + if str(project_root) not in sys.path: + sys.path.insert(0, str(project_root)) + + from chatterbox.tts import ChatterboxTTS +except (ImportError, NameError): + print("Error: Could not import ChatterboxTTS.") + print("Please ensure this script is located in the 'chatterbox' directory and that the main project structure is intact.") + sys.exit(1) + + +# --- Configuration --- + +# 1. Define the texts you want to generate in a batch. +texts_to_generate = [ + "Artists create a sequence of drawings to visualize the animation, shot by shot, much like a comic book. This process helps the entire team understand the director's vision, plan the pacing of the story, and identify potential issues before any resource-intensive digital work commences. It serves as the essential blueprint that guides all subsequent stages of production, ensuring a cohesive final product.", + "Following this, the rigging process gives the models a digital skeleton, or armature, which allows animators to pose and move them realistically. This technical yet artistic step is fundamental for creating believable movement, as a well-constructed rig provides the intuitive controls necessary for bringing static models to life.", + "Simultaneously, the texturing process involves painting and applying surface details, such as skin, fabric, or metal, to the models. These detailed maps, known as textures, determine how light interacts with the surfaces, adding a layer of realism and visual richness. This combination of movement and surface artistry transforms simple geometric shapes into compelling characters.", + "Rendering is the computationally intensive process of calculating the final image from all the data, turning the 3D scene into a sequence of 2D frames. Finally, compositing combines these rendered layers with visual effects and color grading in post-production, seamlessly integrating every component to achieve the final, stunning look of the completed animation.", +] + +# 2. Define the path to the voice prompt audio file. +# This uses the project root to create a reliable path. +AUDIO_PROMPT_PATH = project_root / "assets" / "audio_sample1.wav" + +# 3. Define the output directory for the generated audio files. +OUTPUT_DIR = Path(__file__).resolve().parent / "tts_test_outputs" + + +def main(): + """Main function to run the TTS test.""" + + # --- Device Selection --- + if torch.cuda.is_available(): + device = "cuda" + elif torch.backends.mps.is_available(): + device = "mps" + else: + device = "cpu" + + print(f"Using device: {device}") + + # --- Validate Inputs --- + if not AUDIO_PROMPT_PATH.is_file(): + print(f"Error: Audio prompt file not found at '{AUDIO_PROMPT_PATH}'") + return + + # Create the output directory if it doesn't exist + OUTPUT_DIR.mkdir(exist_ok=True) + print(f"Audio files will be saved in: '{OUTPUT_DIR}'") + + # --- Model Loading --- + print("\nLoading Chatterbox TTS model... (This may take a moment)") + start_time = time.time() + try: + model = ChatterboxTTS.from_pretrained(device=device) + except Exception as e: + print(f"Failed to load model: {e}") + return + load_time = time.time() - start_time + print(f"Model loaded successfully in {load_time:.2f} seconds.") + + # --- Audio Generation --- + print(f"\nGenerating audio for a batch of {len(texts_to_generate)} texts...") + start_time = time.time() + + # The `generate` method handles batching when given a list of strings. + wavs_batch = model.generate( + texts_to_generate, + audio_prompt_path=str(AUDIO_PROMPT_PATH) + ) + + generation_time = time.time() - start_time + print(f"Batch generation completed in {generation_time:.2f} seconds.") + + # --- Saving Outputs --- + print("\nSaving generated audio files...") + for i, wav_tensor in enumerate(wavs_batch): + output_filename = OUTPUT_DIR / f"output_batch_{i+1}.wav" + torchaudio.save(str(output_filename), wav_tensor, model.sr) + print(f" - Saved: {output_filename.name}") + + print("\n--- Test Complete ---") + + +if __name__ == "__main__": + main() \ No newline at end of file From c9132668b37b9d7fb9d4c711d095b60b6cd63f58 Mon Sep 17 00:00:00 2001 From: "DESKTOP-OCNK43A\\Igor" Date: Mon, 20 Oct 2025 14:56:49 +0300 Subject: [PATCH 23/31] added filename comments at the top of each file --- example_tts.py | 2 + example_vc.py | 2 + example_vc_batching.py | 43 ++++++++ gradio_tts_app.py | 2 + gradio_vc_app.py | 2 + run_tts_test.py | 99 +++++++++++++++++++ src/chatterbox/models/s3gen/configs.py | 2 + src/chatterbox/models/s3gen/const.py | 2 + src/chatterbox/models/s3gen/decoder.py | 2 + src/chatterbox/models/s3gen/f0_predictor.py | 2 + src/chatterbox/models/s3gen/flow.py | 2 + src/chatterbox/models/s3gen/flow_matching.py | 2 + src/chatterbox/models/s3gen/hifigan.py | 2 + src/chatterbox/models/s3gen/matcha/decoder.py | 2 + .../models/s3gen/matcha/flow_matching.py | 1 + .../models/s3gen/matcha/text_encoder.py | 2 + .../models/s3gen/matcha/transformer.py | 2 + src/chatterbox/models/s3gen/s3gen.py | 2 + .../models/s3gen/transformer/activation.py | 2 + .../models/s3gen/transformer/attention.py | 2 + .../models/s3gen/transformer/convolution.py | 2 + .../models/s3gen/transformer/embedding.py | 2 + .../models/s3gen/transformer/encoder_layer.py | 2 + .../transformer/positionwise_feed_forward.py | 2 + .../models/s3gen/transformer/subsampling.py | 2 + .../s3gen/transformer/upsample_encoder.py | 2 + .../models/s3gen/utils/class_utils.py | 2 + src/chatterbox/models/s3gen/utils/mask.py | 2 + src/chatterbox/models/s3gen/utils/mel.py | 2 + src/chatterbox/models/s3gen/xvector.py | 2 + .../models/s3tokenizer/s3tokenizer.py | 2 + .../t3/inference/alignment_stream_analyzer.py | 2 + .../models/t3/inference/t3_hf_backend.py | 1 + src/chatterbox/models/t3/llama_configs.py | 2 + src/chatterbox/models/t3/modules/cond_enc.py | 2 + .../models/t3/modules/learned_pos_emb.py | 2 + src/chatterbox/models/t3/modules/perceiver.py | 2 + src/chatterbox/models/t3/modules/t3_config.py | 2 + src/chatterbox/models/t3/t3.py | 2 + src/chatterbox/models/tokenizers/tokenizer.py | 2 + src/chatterbox/models/utils.py | 2 + src/chatterbox/models/voice_encoder/config.py | 2 + .../models/voice_encoder/melspec.py | 1 + .../models/voice_encoder/voice_encoder.py | 1 + src/chatterbox/tts.py | 5 + src/chatterbox/vc.py | 2 + 46 files changed, 229 insertions(+) create mode 100644 example_vc_batching.py create mode 100644 run_tts_test.py diff --git a/example_tts.py b/example_tts.py index f827c13f..0e6c2654 100644 --- a/example_tts.py +++ b/example_tts.py @@ -1,3 +1,5 @@ +# example_tts.py + import torchaudio as ta import torch from chatterbox.tts import ChatterboxTTS diff --git a/example_vc.py b/example_vc.py index 6255a0a3..90d66480 100644 --- a/example_vc.py +++ b/example_vc.py @@ -1,3 +1,5 @@ +# example_vc.py + import torch import torchaudio as ta diff --git a/example_vc_batching.py b/example_vc_batching.py new file mode 100644 index 00000000..1256f509 --- /dev/null +++ b/example_vc_batching.py @@ -0,0 +1,43 @@ +# example_vc_batching.py + +import torchaudio as ta +import torch +from chatterbox.tts import ChatterboxTTS + +# Automatically detect the best available device +if torch.cuda.is_available(): + device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" +else: + device = "cpu" + +print(f"Using device: {device}") + + +model = ChatterboxTTS.from_pretrained(device=device) + +texts_batch = [ "This is the first sentence to be synthesized in a batch.", + "This is the second one." ] + + +# If you want to synthesize with a different voice, specify the audio prompt: +AUDIO_PROMPT_PATH = "YOUR_AUDIO_PROMPT.wav" + + +# Batching - list of strings to synthesize multiple different texts in a single batch. +# This is the most efficient way to process multiple, different prompts at once. +# Careful: 1 text = 1 additional KV Cache (Vram) +wavs_batch = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH) +for i, wav in enumerate(wavs_batch): + ta.save(f"test-batch-{i+1}.wav", wav, model.sr) + +# Batching - Use num_return_sequences to generate multiple variations for each text. +# This is highly efficient for creating diverse samples, as the prompt is only processed once. +# Without making extra KV Caches. +num_variations = 3 + +wavs_batch_multi = model.generate(texts_batch, audio_prompt_path=AUDIO_PROMPT_PATH, num_return_sequences=num_variations) +for i, group in enumerate(wavs_batch_multi): + for j, wav in enumerate(group): + ta.save(f"test-batch-{i+1}-variant-{j+1}.wav", wav, model.sr) \ No newline at end of file diff --git a/gradio_tts_app.py b/gradio_tts_app.py index cda7912b..a313437f 100644 --- a/gradio_tts_app.py +++ b/gradio_tts_app.py @@ -1,3 +1,5 @@ +# gradio_tts_app.py + import random import numpy as np import torch diff --git a/gradio_vc_app.py b/gradio_vc_app.py index 2202e6ba..13c2f219 100644 --- a/gradio_vc_app.py +++ b/gradio_vc_app.py @@ -1,3 +1,5 @@ +# gradio_vc_app.py + import torch import gradio as gr from chatterbox.vc import ChatterboxVC diff --git a/run_tts_test.py b/run_tts_test.py new file mode 100644 index 00000000..12cfda42 --- /dev/null +++ b/run_tts_test.py @@ -0,0 +1,99 @@ +# run_tts_test.py + +import torchaudio +import torch +from pathlib import Path +import time +import sys + +# --- Robust Path Setup --- +# Add the project root to the Python path to allow importing the chatterbox module +try: + # Assuming this script is in the 'chatterbox' directory, the project root is one level up. + project_root = Path(__file__).resolve().parent.parent + if str(project_root) not in sys.path: + sys.path.insert(0, str(project_root)) + + from chatterbox.tts import ChatterboxTTS +except (ImportError, NameError): + print("Error: Could not import ChatterboxTTS.") + print("Please ensure this script is located in the 'chatterbox' directory and that the main project structure is intact.") + sys.exit(1) + + +# --- Configuration --- + +# 1. Define the texts you want to generate in a batch. +texts_to_generate = [ + "Artists create a sequence of drawings to visualize the animation, shot by shot, much like a comic book. This process helps the entire team understand the director's vision, plan the pacing of the story, and identify potential issues before any resource-intensive digital work commences. It serves as the essential blueprint that guides all subsequent stages of production, ensuring a cohesive final product.", + "Following this, the rigging process gives the models a digital skeleton, or armature, which allows animators to pose and move them realistically. This technical yet artistic step is fundamental for creating believable movement, as a well-constructed rig provides the intuitive controls necessary for bringing static models to life.", + "Simultaneously, the texturing process involves painting and applying surface details, such as skin, fabric, or metal, to the models. These detailed maps, known as textures, determine how light interacts with the surfaces, adding a layer of realism and visual richness. This combination of movement and surface artistry transforms simple geometric shapes into compelling characters.", + "Rendering is the computationally intensive process of calculating the final image from all the data, turning the 3D scene into a sequence of 2D frames. Finally, compositing combines these rendered layers with visual effects and color grading in post-production, seamlessly integrating every component to achieve the final, stunning look of the completed animation.", +] + +# 2. Define the path to the voice prompt audio file. +# This uses the project root to create a reliable path. +AUDIO_PROMPT_PATH = project_root / "assets" / "audio_sample1.wav" + +# 3. Define the output directory for the generated audio files. +OUTPUT_DIR = Path(__file__).resolve().parent / "tts_test_outputs" + + +def main(): + """Main function to run the TTS test.""" + + # --- Device Selection --- + if torch.cuda.is_available(): + device = "cuda" + elif torch.backends.mps.is_available(): + device = "mps" + else: + device = "cpu" + + print(f"Using device: {device}") + + # --- Validate Inputs --- + if not AUDIO_PROMPT_PATH.is_file(): + print(f"Error: Audio prompt file not found at '{AUDIO_PROMPT_PATH}'") + return + + # Create the output directory if it doesn't exist + OUTPUT_DIR.mkdir(exist_ok=True) + print(f"Audio files will be saved in: '{OUTPUT_DIR}'") + + # --- Model Loading --- + print("\nLoading Chatterbox TTS model... (This may take a moment)") + start_time = time.time() + try: + model = ChatterboxTTS.from_pretrained(device=device) + except Exception as e: + print(f"Failed to load model: {e}") + return + load_time = time.time() - start_time + print(f"Model loaded successfully in {load_time:.2f} seconds.") + + # --- Audio Generation --- + print(f"\nGenerating audio for a batch of {len(texts_to_generate)} texts...") + start_time = time.time() + + # The `generate` method handles batching when given a list of strings. + wavs_batch = model.generate( + texts_to_generate, + audio_prompt_path=str(AUDIO_PROMPT_PATH) + ) + + generation_time = time.time() - start_time + print(f"Batch generation completed in {generation_time:.2f} seconds.") + + # --- Saving Outputs --- + print("\nSaving generated audio files...") + for i, wav_tensor in enumerate(wavs_batch): + output_filename = OUTPUT_DIR / f"output_batch_{i+1}.wav" + torchaudio.save(str(output_filename), wav_tensor, model.sr) + print(f" - Saved: {output_filename.name}") + + print("\n--- Test Complete ---") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/chatterbox/models/s3gen/configs.py b/src/chatterbox/models/s3gen/configs.py index b09b2e52..c243fc34 100644 --- a/src/chatterbox/models/s3gen/configs.py +++ b/src/chatterbox/models/s3gen/configs.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/configs.py + from ..utils import AttrDict CFM_PARAMS = AttrDict({ diff --git a/src/chatterbox/models/s3gen/const.py b/src/chatterbox/models/s3gen/const.py index 72de6a23..78811b81 100644 --- a/src/chatterbox/models/s3gen/const.py +++ b/src/chatterbox/models/s3gen/const.py @@ -1 +1,3 @@ +#chatterbox/src/chatterbox/models/s3gen/const.py + S3GEN_SR = 24000 diff --git a/src/chatterbox/models/s3gen/decoder.py b/src/chatterbox/models/s3gen/decoder.py index c568c2df..dc2b3049 100644 --- a/src/chatterbox/models/s3gen/decoder.py +++ b/src/chatterbox/models/s3gen/decoder.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/decoder.py + # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/src/chatterbox/models/s3gen/f0_predictor.py b/src/chatterbox/models/s3gen/f0_predictor.py index 172c5f50..00a6b3a7 100644 --- a/src/chatterbox/models/s3gen/f0_predictor.py +++ b/src/chatterbox/models/s3gen/f0_predictor.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/f0_predictor.py + # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu) # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/src/chatterbox/models/s3gen/flow.py b/src/chatterbox/models/s3gen/flow.py index ad19cfa3..14e267f6 100644 --- a/src/chatterbox/models/s3gen/flow.py +++ b/src/chatterbox/models/s3gen/flow.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/flow.py + # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/src/chatterbox/models/s3gen/flow_matching.py b/src/chatterbox/models/s3gen/flow_matching.py index ecd69fa4..5c705e6c 100644 --- a/src/chatterbox/models/s3gen/flow_matching.py +++ b/src/chatterbox/models/s3gen/flow_matching.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/flow_matching.py + # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/src/chatterbox/models/s3gen/hifigan.py b/src/chatterbox/models/s3gen/hifigan.py index 33f9387e..de7256db 100644 --- a/src/chatterbox/models/s3gen/hifigan.py +++ b/src/chatterbox/models/s3gen/hifigan.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/hifigan.py + # jrm: adapted from CosyVoice/cosyvoice/hifigan/generator.py # most modules should be reusable, but I found their SineGen changed a git. diff --git a/src/chatterbox/models/s3gen/matcha/decoder.py b/src/chatterbox/models/s3gen/matcha/decoder.py index 6919f32d..b18b3c18 100644 --- a/src/chatterbox/models/s3gen/matcha/decoder.py +++ b/src/chatterbox/models/s3gen/matcha/decoder.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/decoder.py + import math from typing import Optional diff --git a/src/chatterbox/models/s3gen/matcha/flow_matching.py b/src/chatterbox/models/s3gen/matcha/flow_matching.py index add7b08c..eefe78cb 100644 --- a/src/chatterbox/models/s3gen/matcha/flow_matching.py +++ b/src/chatterbox/models/s3gen/matcha/flow_matching.py @@ -1,3 +1,4 @@ +#chatterbox/src/chatterbox/models/s3gen/flow_matching.py from abc import ABC import torch diff --git a/src/chatterbox/models/s3gen/matcha/text_encoder.py b/src/chatterbox/models/s3gen/matcha/text_encoder.py index 276eee73..9d8fbb1c 100644 --- a/src/chatterbox/models/s3gen/matcha/text_encoder.py +++ b/src/chatterbox/models/s3gen/matcha/text_encoder.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/text_encoder.py + """ from https://github.com/jaywalnut310/glow-tts """ import math diff --git a/src/chatterbox/models/s3gen/matcha/transformer.py b/src/chatterbox/models/s3gen/matcha/transformer.py index dd1afa3a..5bc77767 100644 --- a/src/chatterbox/models/s3gen/matcha/transformer.py +++ b/src/chatterbox/models/s3gen/matcha/transformer.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/matcha/transformer.py + from typing import Any, Dict, Optional import torch diff --git a/src/chatterbox/models/s3gen/s3gen.py b/src/chatterbox/models/s3gen/s3gen.py index b1cf05e6..0dc70008 100644 --- a/src/chatterbox/models/s3gen/s3gen.py +++ b/src/chatterbox/models/s3gen/s3gen.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/s3gen.py + # Modified from CosyVoice https://github.com/FunAudioLLM/CosyVoice # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/src/chatterbox/models/s3gen/transformer/activation.py b/src/chatterbox/models/s3gen/transformer/activation.py index 8cea5481..00180d0d 100644 --- a/src/chatterbox/models/s3gen/transformer/activation.py +++ b/src/chatterbox/models/s3gen/transformer/activation.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/activation.py + # Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe) # 2020 Northwestern Polytechnical University (Pengcheng Guo) # 2020 Mobvoi Inc (Binbin Zhang) diff --git a/src/chatterbox/models/s3gen/transformer/attention.py b/src/chatterbox/models/s3gen/transformer/attention.py index 95e1d840..046b7c7c 100644 --- a/src/chatterbox/models/s3gen/transformer/attention.py +++ b/src/chatterbox/models/s3gen/transformer/attention.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/attention.py + # Copyright (c) 2019 Shigeki Karita # 2020 Mobvoi Inc (Binbin Zhang) # 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) diff --git a/src/chatterbox/models/s3gen/transformer/convolution.py b/src/chatterbox/models/s3gen/transformer/convolution.py index 4d5d9614..7f6d963b 100644 --- a/src/chatterbox/models/s3gen/transformer/convolution.py +++ b/src/chatterbox/models/s3gen/transformer/convolution.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/convolution.py + # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) # 2024 Alibaba Inc (Xiang Lyu) # diff --git a/src/chatterbox/models/s3gen/transformer/embedding.py b/src/chatterbox/models/s3gen/transformer/embedding.py index eae8c8ec..bb662e4a 100644 --- a/src/chatterbox/models/s3gen/transformer/embedding.py +++ b/src/chatterbox/models/s3gen/transformer/embedding.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/embedding.py + # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu) # 2024 Alibaba Inc (Xiang Lyu) # diff --git a/src/chatterbox/models/s3gen/transformer/encoder_layer.py b/src/chatterbox/models/s3gen/transformer/encoder_layer.py index efbb12dd..636b884f 100644 --- a/src/chatterbox/models/s3gen/transformer/encoder_layer.py +++ b/src/chatterbox/models/s3gen/transformer/encoder_layer.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/encoder_layer.py + # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) # 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) # diff --git a/src/chatterbox/models/s3gen/transformer/positionwise_feed_forward.py b/src/chatterbox/models/s3gen/transformer/positionwise_feed_forward.py index b7a2cf6e..9a05a31f 100644 --- a/src/chatterbox/models/s3gen/transformer/positionwise_feed_forward.py +++ b/src/chatterbox/models/s3gen/transformer/positionwise_feed_forward.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/positionwise_feed_forward.py + # Copyright (c) 2019 Shigeki Karita # 2020 Mobvoi Inc (Binbin Zhang) # diff --git a/src/chatterbox/models/s3gen/transformer/subsampling.py b/src/chatterbox/models/s3gen/transformer/subsampling.py index e17c2e32..df570b14 100644 --- a/src/chatterbox/models/s3gen/transformer/subsampling.py +++ b/src/chatterbox/models/s3gen/transformer/subsampling.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/subsampling.py + # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) # 2024 Alibaba Inc (Xiang Lyu) # diff --git a/src/chatterbox/models/s3gen/transformer/upsample_encoder.py b/src/chatterbox/models/s3gen/transformer/upsample_encoder.py index 766a5e4e..2af7ef39 100644 --- a/src/chatterbox/models/s3gen/transformer/upsample_encoder.py +++ b/src/chatterbox/models/s3gen/transformer/upsample_encoder.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/transformer/upsample_encoder.py + # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu) # 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn) # 2024 Alibaba Inc (Xiang Lyu) diff --git a/src/chatterbox/models/s3gen/utils/class_utils.py b/src/chatterbox/models/s3gen/utils/class_utils.py index cd31e480..2e176564 100644 --- a/src/chatterbox/models/s3gen/utils/class_utils.py +++ b/src/chatterbox/models/s3gen/utils/class_utils.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/utils/class_utils.py + # Copyright [2023-11-28] # 2024 Alibaba Inc (authors: Xiang Lyu) # diff --git a/src/chatterbox/models/s3gen/utils/mask.py b/src/chatterbox/models/s3gen/utils/mask.py index 08c97a3e..faa63f84 100644 --- a/src/chatterbox/models/s3gen/utils/mask.py +++ b/src/chatterbox/models/s3gen/utils/mask.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/utils/mask.py + # Copyright (c) 2019 Shigeki Karita # 2020 Mobvoi Inc (Binbin Zhang) # 2024 Alibaba Inc (authors: Xiang Lyu) diff --git a/src/chatterbox/models/s3gen/utils/mel.py b/src/chatterbox/models/s3gen/utils/mel.py index 907d2b57..d020cc8d 100644 --- a/src/chatterbox/models/s3gen/utils/mel.py +++ b/src/chatterbox/models/s3gen/utils/mel.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/utils/mel.py + """mel-spectrogram extraction in Matcha-TTS""" import logging from librosa.filters import mel as librosa_mel_fn diff --git a/src/chatterbox/models/s3gen/xvector.py b/src/chatterbox/models/s3gen/xvector.py index 6eb99af4..cb165e3d 100644 --- a/src/chatterbox/models/s3gen/xvector.py +++ b/src/chatterbox/models/s3gen/xvector.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/s3gen/xvector.py + #!/usr/bin/env python3 # -*- encoding: utf-8 -*- # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. diff --git a/src/chatterbox/models/s3tokenizer/s3tokenizer.py b/src/chatterbox/models/s3tokenizer/s3tokenizer.py index 8648608a..b7aff5ad 100644 --- a/src/chatterbox/models/s3tokenizer/s3tokenizer.py +++ b/src/chatterbox/models/s3tokenizer/s3tokenizer.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/s3tokenizer/s3tokenizer.py + from typing import List, Tuple import numpy as np diff --git a/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py b/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py index 255d50f0..03115eb5 100644 --- a/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py +++ b/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/inference/alignment_stream_analyzer.py + # Copyright (c) 2025 Resemble AI # Author: John Meade, Jeremy Hsu # MIT License diff --git a/src/chatterbox/models/t3/inference/t3_hf_backend.py b/src/chatterbox/models/t3/inference/t3_hf_backend.py index 69a6bf20..031b1a12 100644 --- a/src/chatterbox/models/t3/inference/t3_hf_backend.py +++ b/src/chatterbox/models/t3/inference/t3_hf_backend.py @@ -1,3 +1,4 @@ +#chatterbox/src/chatterbox/models/t3/inference/t3_hf_backend.py from typing import Optional import torch diff --git a/src/chatterbox/models/t3/llama_configs.py b/src/chatterbox/models/t3/llama_configs.py index 14d06816..611c516f 100644 --- a/src/chatterbox/models/t3/llama_configs.py +++ b/src/chatterbox/models/t3/llama_configs.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/llama_configs.py + LLAMA_520M_CONFIG_DICT = dict( # Arbitrary small number that won't cause problems when loading. # These param are unused due to custom input layers. diff --git a/src/chatterbox/models/t3/modules/cond_enc.py b/src/chatterbox/models/t3/modules/cond_enc.py index b5f15c68..65fa2f93 100644 --- a/src/chatterbox/models/t3/modules/cond_enc.py +++ b/src/chatterbox/models/t3/modules/cond_enc.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/modules/cond_enc.py + from dataclasses import dataclass from typing import Optional diff --git a/src/chatterbox/models/t3/modules/learned_pos_emb.py b/src/chatterbox/models/t3/modules/learned_pos_emb.py index 9b197f21..536b9d6f 100644 --- a/src/chatterbox/models/t3/modules/learned_pos_emb.py +++ b/src/chatterbox/models/t3/modules/learned_pos_emb.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/modules/learned_pos_emb.py + from typing import Union import torch diff --git a/src/chatterbox/models/t3/modules/perceiver.py b/src/chatterbox/models/t3/modules/perceiver.py index be9c5b86..99e562d1 100644 --- a/src/chatterbox/models/t3/modules/perceiver.py +++ b/src/chatterbox/models/t3/modules/perceiver.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/modules/perceiver.py + # Copyright (c) 2025 Resemble AI # Author: Manmay Nakhashi # MIT License diff --git a/src/chatterbox/models/t3/modules/t3_config.py b/src/chatterbox/models/t3/modules/t3_config.py index 55b43903..81a46583 100644 --- a/src/chatterbox/models/t3/modules/t3_config.py +++ b/src/chatterbox/models/t3/modules/t3_config.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/modules/t3_config.py + from ..llama_configs import LLAMA_CONFIGS diff --git a/src/chatterbox/models/t3/t3.py b/src/chatterbox/models/t3/t3.py index 905566c6..c014687c 100644 --- a/src/chatterbox/models/t3/t3.py +++ b/src/chatterbox/models/t3/t3.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/t3/t3.py + # Copyright (c) 2025 Resemble AI # MIT License import logging diff --git a/src/chatterbox/models/tokenizers/tokenizer.py b/src/chatterbox/models/tokenizers/tokenizer.py index 84d45d35..15641a03 100644 --- a/src/chatterbox/models/tokenizers/tokenizer.py +++ b/src/chatterbox/models/tokenizers/tokenizer.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/tokenizers/tokenizer.py + import logging import json diff --git a/src/chatterbox/models/utils.py b/src/chatterbox/models/utils.py index a4abce5d..e0e1d2ee 100644 --- a/src/chatterbox/models/utils.py +++ b/src/chatterbox/models/utils.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/utils.py + class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) diff --git a/src/chatterbox/models/voice_encoder/config.py b/src/chatterbox/models/voice_encoder/config.py index 8e9782a2..b3bec4b7 100644 --- a/src/chatterbox/models/voice_encoder/config.py +++ b/src/chatterbox/models/voice_encoder/config.py @@ -1,3 +1,5 @@ +#chatterbox/src/chatterbox/models/voice_encoder/config.py + class VoiceEncConfig: num_mels = 40 sample_rate = 16000 diff --git a/src/chatterbox/models/voice_encoder/melspec.py b/src/chatterbox/models/voice_encoder/melspec.py index 69147fc8..ac18b474 100644 --- a/src/chatterbox/models/voice_encoder/melspec.py +++ b/src/chatterbox/models/voice_encoder/melspec.py @@ -1,3 +1,4 @@ +#chatterbox/src/chatterbox/models/voice_encoder/melspec.py from functools import lru_cache from scipy import signal diff --git a/src/chatterbox/models/voice_encoder/voice_encoder.py b/src/chatterbox/models/voice_encoder/voice_encoder.py index d986f17f..9bcf337d 100644 --- a/src/chatterbox/models/voice_encoder/voice_encoder.py +++ b/src/chatterbox/models/voice_encoder/voice_encoder.py @@ -1,3 +1,4 @@ +#chatterbox/src/chatterbox/models/voice_encoder/voice_encoder.py # Adapted from https://github.com/CorentinJ/Real-Time-Voice-Cloning # MIT License from typing import List, Union, Optional diff --git a/src/chatterbox/tts.py b/src/chatterbox/tts.py index 6d9b5ad5..8f77f4bd 100644 --- a/src/chatterbox/tts.py +++ b/src/chatterbox/tts.py @@ -1,3 +1,8 @@ +<<<<<<< Updated upstream +======= +# chatterbox/src/chatterbox/tts.py + +>>>>>>> Stashed changes from dataclasses import dataclass from pathlib import Path diff --git a/src/chatterbox/vc.py b/src/chatterbox/vc.py index a9c32ed3..e7885a62 100644 --- a/src/chatterbox/vc.py +++ b/src/chatterbox/vc.py @@ -1,3 +1,5 @@ +# chatterbox/src/chatterbox/vc.py + from pathlib import Path import librosa From f100e83941fc7261f077091ec83be8e59e51f968 Mon Sep 17 00:00:00 2001 From: "DESKTOP-OCNK43A\\Igor" Date: Mon, 20 Oct 2025 15:08:42 +0300 Subject: [PATCH 24/31] Update chatterbox.pyproj --- chatterbox.pyproj | 146 +++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 144 insertions(+), 2 deletions(-) diff --git a/chatterbox.pyproj b/chatterbox.pyproj index 80ff3e71..a5f2b4c4 100644 --- a/chatterbox.pyproj +++ b/chatterbox.pyproj @@ -4,13 +4,15 @@ 2.0 958ac2fa-ebae-40f7-bf36-e239ba9b0efd . - chatterbox.py + + . . chatterbox chatterbox + true true @@ -21,7 +23,147 @@ false - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +