main.py

# Profiling
# import cProfile as profile
# import pstats
# Logging
import os
import logging
# FastAPI preprocessor
from fastapi import FastAPI, UploadFile, Request, Response, status, WebSocket, WebSocketDisconnect
from fastapi.responses import JSONResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import PlainTextResponse
from typing import Optional, Tuple, List
from pydantic import BaseModel
import types
import random
import base64
import binascii
import json
import datetime
import numpy as np
import warnings
import io
import re
import math
import functools
from typing import NamedTuple

# WebRTC
import asyncio

from aiortc import RTCPeerConnection, RTCSessionDescription, RTCRtpReceiver
from aiortc.rtp import RtcpByePacket
from wis.media import MediaRecorderLite


# Whisper
import ctranslate2
import librosa
import transformers
import wis.languages

# TTS
import soundfile as sf
from speechbrain.pretrained import EncoderClassifier
import torchaudio
import tempfile
import shutil
from num2words import num2words
import mimetypes

import torch

# SV
from torchaudio.sox_effects import apply_effects_tensor
import operator

# Import audio stuff adapted from ref Whisper implementation
from wis.audio import log_mel_spectrogram, pad_or_trim, chunk_iter, find_longest_common_sequence

# Willow
import wave
import av

mimetypes.init()
logger = logging.getLogger("infer")
gunicorn_logger = logging.getLogger('gunicorn.error')
logger.handlers = gunicorn_logger.handlers
logger.setLevel(gunicorn_logger.level)

try:
    from custom_settings import get_api_settings
    settings = get_api_settings()
    logger.info(f"{settings.name} is starting with custom settings... Please wait.")
except Exception:
    from settings import get_api_settings
    settings = get_api_settings()
    logger.info(f"{settings.name} is starting... Please wait.")

warnings.simplefilter(action='ignore')

pcs = set()

# Whisper supported languages
whisper_languages = wis.languages.LANGUAGES

# Use soundfile so we can support WAV, FLAC, etc
torchaudio.set_audio_backend('soundfile')


# Function to create a wav file from stream data
def write_stream_wav(data, rate, bits, ch):
    file = io.BytesIO()
    wavfile = wave.open(file, 'wb')
    wavfile.setparams((ch, int(bits/8), rate, 0, 'NONE', 'NONE'))
    wavfile.writeframesraw(bytearray(data))
    wavfile.close()
    file.seek(0)
    return file


def audio_to_wav(file, rate: 16000):
    """Arbitrary media files to wav"""
    wav = io.BytesIO()
    with av.open(file) as in_container:
        in_stream = in_container.streams.audio[0]
        with av.open(wav, 'w', 'wav') as out_container:
            out_stream = out_container.add_stream(
                'pcm_s16le',
                rate=rate,
                layout='mono'
            )
            for frame in in_container.decode(in_stream):
                for packet in out_stream.encode(frame):
                    out_container.mux(packet)

    wav.seek(0)
    return wav


# Monkey patch aiortc
# sender.replaceTrack(null) sends a RtcpByePacket which we want to ignore
# in this case and keep connection open. XXX: Are there other cases we want to close?
old_handle_rtcp_packet = RTCRtpReceiver._handle_rtcp_packet


async def new_handle_rtcp_packet(self, packet):
    if isinstance(packet, RtcpByePacket):
        return
    return old_handle_rtcp_packet(self, packet)
RTCRtpReceiver._handle_rtcp_packet = new_handle_rtcp_packet

if settings.aiortc_debug:
    logger.debug('AIORTC: Debugging active')
    logging.basicConfig(level=logging.DEBUG)  # very useful debugging aiortc issues

local_ports = list(range(10000, 10000+50))  # Allowed ephemeral port range


def patch_loop_datagram():
    loop = asyncio.get_event_loop()
    if getattr(loop, '_patch_done', False):
        return

    # Monkey patch aiortc to control ephemeral ports
    old_create_datagram_endpoint = loop.create_datagram_endpoint

    async def create_datagram_endpoint(self, protocol_factory, local_addr: Tuple[str, int] = None, **kwargs):
        # if port is specified just use it
        if local_addr and local_addr[1]:
            return await old_create_datagram_endpoint(protocol_factory, local_addr=local_addr, **kwargs)
        if local_addr is None:
            return await old_create_datagram_endpoint(protocol_factory, local_addr=None, **kwargs)
        # if port is not specified make it use our range
        ports = list(local_ports)
        random.shuffle(ports)
        for port in ports:
            try:
                ret = await old_create_datagram_endpoint(
                    protocol_factory, local_addr=(local_addr[0], port), **kwargs
                )
                logger.debug(f'create_datagram_endpoint chose port {port}')
                return ret
            except OSError as exc:
                if port == ports[-1]:
                    # this was the last port, give up
                    raise exc
        raise ValueError("local_ports must not be empty")
    loop.create_datagram_endpoint = types.MethodType(create_datagram_endpoint, loop)
    loop._patch_done = True


patch_loop_datagram()  # not really needed here...

# XXX: rm these globals and use settings directly

# default beam_size - 5 is lib default, 1 for greedy
beam_size = settings.beam_size

# default beam size for longer transcriptions
long_beam_size = settings.long_beam_size
# Audio duration in ms to activate "long" mode
long_beam_size_threshold = settings.long_beam_size_threshold

# model threads
model_threads = settings.model_threads

# Support for chunking
support_chunking = settings.support_chunking

# Support for TTS
support_tts = settings.support_tts

# Support for SV
support_sv = settings.support_sv

# Support chatbot
support_chatbot = settings.support_chatbot

# Default SV threshold
sv_threshold = settings.sv_threshold

whisper_model_default = settings.whisper_model_default

tts_default_format = settings.tts_default_format

tts_default_speaker = settings.tts_default_speaker

# Default language
language = settings.language

# Default detect language?
detect_language = settings.detect_language

# Path to chatbot model
chatbot_model_path = settings.chatbot_model_path

# Chatbot model basename
chatbot_model_basename = settings.chatbot_model_basename

# Chatbot device
chatbot_device = settings.chatbot_device

# Chatbot temperature
chatbot_temperature = settings.chatbot_temperature

# Chatbot top_p
chatbot_top_p = settings.chatbot_top_p

# Chatbot model repetition penalty
chatbot_repetition_penalty = settings.chatbot_repetition_penalty

# Chatbot model max length
chatbot_max_new_tokens = settings.chatbot_max_new_tokens

# Try CUDA
device = "cuda" if torch.cuda.is_available() else "cpu"

if device == "cuda":
    cuda_num_devices = torch.cuda.device_count()
    logger.info(f'CUDA: Detected {cuda_num_devices} device(s)')

    # Assume Volta or higher
    compute_type = "int8_float16"

    for cuda_dev_num in range(0, cuda_num_devices, 1):
        # Print CUDA device name
        cuda_device_name = torch.cuda.get_device_name(cuda_dev_num)
        logger.info(f'CUDA: Device {cuda_dev_num} name: {cuda_device_name}')

        # Get CUDA device capability
        cuda_device_capability = torch.cuda.get_device_capability(cuda_dev_num)
        cuda_device_capability = functools.reduce(lambda sub, ele: sub * 10 + ele, cuda_device_capability)
        logger.info(f'CUDA: Device {cuda_dev_num} capability: {cuda_device_capability}')

        # Get CUDA memory - returns in bytes
        cuda_total_memory = torch.cuda.mem_get_info(cuda_dev_num)[1]
        cuda_free_memory = torch.cuda.mem_get_info(cuda_dev_num)[0]
        logger.info(f'CUDA: Device {cuda_dev_num} total memory: {cuda_total_memory} bytes')
        logger.info(f'CUDA: Device {cuda_dev_num} free memory: {cuda_free_memory} bytes')

        # Disable chunking if card has less than 10GB VRAM (complete guess)
        # This can still encounter out of memory errors depending on audio length
        if cuda_free_memory <= 10000000000:
            logger.warning(f'CUDA: Device {cuda_dev_num} has low memory, disabling chunking support')
            support_chunking = False

        if cuda_free_memory <= settings.tts_memory_threshold:
            logger.warning(f'CUDA: Device {cuda_dev_num} has low memory, disabling TTS support')
            support_tts = False

        if cuda_free_memory <= settings.sv_memory_threshold:
            logger.warning(f'CUDA: Device {cuda_dev_num} has low memory, disabling SV support')
            support_sv = False

        # Override compute_type if at least one non-Volta card and override+warn on pre-pascal devices
        if cuda_device_capability in range(1, 59):
            logger.warning(f'CUDA: Device {cuda_dev_num} is pre-Pascal, forcing float32')
            logger.warning('CUDA: SUPPORT FOR PRE-PASCAL DEVICES IS UNSUPPORTED AND WILL BE REMOVED IN THE FUTURE')
            compute_type = "float32"
        elif cuda_device_capability < 70:
            logger.warning(f'CUDA: Device {cuda_dev_num} is pre-Volta, forcing int8')
            compute_type = "int8"

        # Disable chatbot pre-Volta or low VRAM
        if cuda_device_capability < 70 and support_chatbot:
            logger.warning(f'CUDA: Device {cuda_dev_num} is pre-Volta, disabling chatbot')
            support_chatbot = False

        if cuda_free_memory <= 12000000000 and support_chatbot:
            logger.warning(f'CUDA: Device {cuda_dev_num} has low memory, disabling chatbot support')
            support_chatbot = False

    # Set ctranslate device index based on number of supported devices
    device_index = [*range(0, cuda_num_devices, 1)]
else:
    num_cpu_cores = os.cpu_count()
    compute_type = "int8"
    # Tested to generally perform best on CPU
    intra_threads = num_cpu_cores // 2
    model_threads = num_cpu_cores // 2
    logger.info(f'CUDA: Not found - using CPU with {num_cpu_cores} cores')

# These models refuse to cooperate otherwise
if support_sv:
    logger.info("Loading SV models...")
    sv_feature_extractor = transformers.Wav2Vec2FeatureExtractor.from_pretrained(
            "./models/microsoft-wavlm-base-plus-sv")
    sv_model = transformers.WavLMForXVector.from_pretrained("./models/microsoft-wavlm-base-plus-sv").to(device=device)
else:
    sv_feature_extractor = None
    sv_model = None


class Models(NamedTuple):
    whisper_processor: any
    whisper_model_tiny: any
    whisper_model_base: any
    whisper_model_small: any
    whisper_model_medium: any
    whisper_model_large: any

    tts_processor: any
    tts_model: any
    tts_vocoder: any

    chatbot_tokenizer: any
    chatbot_model: any


models: Models = None


def load_models() -> Models:
    global models
    # Turn up log_level for ctranslate2
    # ctranslate2.set_log_level(logger.DEBUG)
    # Load processor from transformers
    whisper_processor = transformers.WhisperProcessor.from_pretrained("./models/tovera-wis-whisper-base")
    # Show supported compute types
    supported_compute_types = str(ctranslate2.get_supported_compute_types(device))
    logger.info(f'CTRANSLATE: Supported compute types for device {device} are {supported_compute_types}'
                f'- using configured {compute_type}')

    # Load all models - thanks for quantization ctranslate2
    logger.info("Loading Whisper models...")
    if device == "cuda":
        whisper_model_tiny = ctranslate2.models.Whisper('models/tovera-wis-whisper-tiny', device=device,
                                                        compute_type=compute_type, device_index=device_index,
                                                        inter_threads=model_threads)
        whisper_model_base = ctranslate2.models.Whisper('models/tovera-wis-whisper-base', device=device,
                                                        compute_type=compute_type, device_index=device_index,
                                                        inter_threads=model_threads)
        whisper_model_small = ctranslate2.models.Whisper('models/tovera-wis-whisper-small', device=device,
                                                         compute_type=compute_type, device_index=device_index,
                                                         inter_threads=model_threads)
        whisper_model_medium = ctranslate2.models.Whisper('models/tovera-wis-whisper-medium', device=device,
                                                          compute_type=compute_type, device_index=device_index,
                                                          inter_threads=model_threads)
        whisper_model_large = ctranslate2.models.Whisper('models/tovera-wis-whisper-large-v2', device=device,
                                                         compute_type=compute_type, device_index=device_index,
                                                         inter_threads=model_threads)
    else:
        whisper_model_tiny = ctranslate2.models.Whisper('models/tovera-wis-whisper-tiny', device=device,
                                                        compute_type=compute_type, inter_threads=model_threads,
                                                        intra_threads=intra_threads)
        whisper_model_base = ctranslate2.models.Whisper('models/tovera-wis-whisper-base', device=device,
                                                        compute_type=compute_type, inter_threads=model_threads,
                                                        intra_threads=intra_threads)
        whisper_model_small = ctranslate2.models.Whisper('models/tovera-wis-whisper-small', device=device,
                                                         compute_type=compute_type, inter_threads=model_threads,
                                                         intra_threads=intra_threads)
        whisper_model_medium = ctranslate2.models.Whisper('models/tovera-wis-whisper-medium', device=device,
                                                          compute_type=compute_type, inter_threads=model_threads,
                                                          intra_threads=intra_threads)
        whisper_model_large = ctranslate2.models.Whisper('models/tovera-wis-whisper-large-v2', device=device,
                                                         compute_type=compute_type, inter_threads=model_threads,
                                                         intra_threads=intra_threads)

    if support_tts:
        logger.info("Loading TTS models...")
        tts_processor = transformers.SpeechT5Processor.from_pretrained("./models/microsoft-speecht5_tts")
        tts_model = transformers.SpeechT5ForTextToSpeech.from_pretrained(
            "./models/microsoft-speecht5_tts").to(device=device)
        tts_vocoder = transformers.SpeechT5HifiGan.from_pretrained(
            "./models/microsoft-speecht5_hifigan").to(device=device)
    else:
        tts_processor = None
        tts_model = None
        tts_vocoder = None

    if support_chatbot and device == "cuda":
        logger.info(f'CHATBOT: Using model {chatbot_model_path} and CUDA, attempting load (this takes a while)...')
        from transformers import AutoTokenizer
        from auto_gptq import AutoGPTQForCausalLM

        chatbot_tokenizer = AutoTokenizer.from_pretrained(chatbot_model_path, use_fast=True)

        # load quantized model, currently only support single gpu
        chatbot_model = AutoGPTQForCausalLM.from_quantized(chatbot_model_path,
                                                           model_basename=chatbot_model_basename,
                                                           use_safetensors=True,
                                                           trust_remote_code=False,
                                                           device=chatbot_device,
                                                           use_triton=True,
                                                           quantize_config=None)

    else:
        chatbot_tokenizer = None
        chatbot_model = None

    models = Models(whisper_processor, whisper_model_tiny, whisper_model_base, whisper_model_small,
                    whisper_model_medium, whisper_model_large, tts_processor, tts_model, tts_vocoder,
                    chatbot_tokenizer, chatbot_model)
    return models


def warm_models():
    if device == "cuda":
        logger.info("Warming models...")
        for x in range(3):
            if models.whisper_model_tiny is not None:
                do_whisper("client/3sec.flac", "tiny", beam_size, "transcribe", False, "en")
            if models.whisper_model_base is not None:
                do_whisper("client/3sec.flac", "base", beam_size, "transcribe", False, "en")
            if models.whisper_model_small is not None:
                do_whisper("client/3sec.flac", "small", beam_size, "transcribe", False, "en")
            if models.whisper_model_medium is not None:
                do_whisper("client/3sec.flac", "medium", beam_size, "transcribe", False, "en")
            if models.whisper_model_large is not None:
                do_whisper("client/3sec.flac", "large", beam_size, "transcribe", False, "en")
            if sv_model is not None:
                do_sv("client/3sec.flac")

            if models.tts_model is not None:
                logger.info("Warming TTS... This takes a while on first run.")
                do_tts("Hello from Willow")

        # Warm chatbot once
        if models.chatbot_model is not None:
            logger.info("Warming chatbot... This takes a while on first run.")
            do_chatbot("Tell me about AI")
    else:
        logger.info("Skipping warm_models for CPU")
        return


def do_chatbot(text, max_new_tokens=chatbot_max_new_tokens, temperature=chatbot_temperature, top_p=chatbot_top_p,
               repetition_penalty=chatbot_repetition_penalty):
    if models.chatbot_model is not None:
        first_time_start = datetime.datetime.now()
        logger.debug(f'CHATBOT: Input is: {text}')
        prompt = f'''USER: {text}
ASSISTANT:'''
        logger.debug(f'CHATBOT: Pipeline parameters are max_new_tokens {max_new_tokens} temperature {temperature}'
                     f'top_p {top_p} repetition_penalty {repetition_penalty}')
        chatbot_pipeline = transformers.pipeline(
            "text-generation",
            model=models.chatbot_model,
            tokenizer=models.chatbot_tokenizer,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=repetition_penalty)

        output = chatbot_pipeline(prompt)[0]["generated_text"]

        # Split so we don't return anything other than response
        try:
            output = output.split("ASSISTANT: ")[1]
        except Exception:
            logger.warning('CHATBOT: Response did not have assistant format')

        time_end = datetime.datetime.now()
        infer_time = time_end - first_time_start
        infer_time_milliseconds = infer_time.total_seconds() * 1000
        logger.debug(f'CHATBOT: Response is: {output}')
        logger.debug('CHATBOT: Response took ' + str(infer_time_milliseconds) + ' ms')
    else:
        logger.warning('CHATBOT: Not installed or supported')
        output = "Chatbot not installed or supported"
    return output


def do_translate(whisper_model, features, batch_size, language, beam_size):
    # Set task in token format for processor
    task = 'translate'
    logger.debug(f'WHISPER: Doing translation with {language} beam size {beam_size} and batch size {batch_size}')
    processor_task = f'<|{task}|>'

    # Describe the task in the prompt.
    # See the prompt format in https://github.com/openai/whisper.
    prompt = models.whisper_processor.tokenizer.convert_tokens_to_ids(
        [
            "<|startoftranscript|>",
            language,
            processor_task,
            "<|notimestamps|>",  # Remove this token to generate timestamps.
        ]
    )

    # Run generation for the 30-second window.
    time_start = datetime.datetime.now()
    results = whisper_model.generate(features, [prompt]*batch_size, beam_size=beam_size)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Translate inference took ' + str(infer_time_milliseconds) + ' ms')
    results = models.whisper_processor.decode(results[0].sequences_ids[0])
    logger.debug(results)

    return results


def check_language(language):
    return language in whisper_languages


def do_whisper(audio_file, model: str, beam_size: int = beam_size, task: str = "transcribe",
               detect_language: bool = False, force_language: str = None, translate: bool = False):
    # Point to model object depending on passed model string
    if model == "large":
        whisper_model = models.whisper_model_large
    elif model == "medium":
        whisper_model = models.whisper_model_medium
    elif model == "small":
        whisper_model = models.whisper_model_small
    elif model == "base":
        whisper_model = models.whisper_model_base
    elif model == "tiny":
        whisper_model = models.whisper_model_tiny

    processor_task = f'<|{task}|>'
    first_time_start = datetime.datetime.now()

    # Whisper STEP 1 - load audio and extract features
    audio, audio_sr = librosa.load(audio_file, sr=16000, mono=True)
    audio_duration = librosa.get_duration(y=audio, sr=audio_sr) * 1000
    audio_duration = int(audio_duration)
    if audio_duration >= long_beam_size_threshold:
        logger.debug(f'WHISPER: Audio duration is {audio_duration} ms - activating long mode')
        beam_size = long_beam_size
    use_chunking = False
    if audio_duration > 30*1000:
        if support_chunking:
            logger.debug('WHISPER: Audio duration is > 30s - activating chunking')
            use_chunking = True
        else:
            logger.warning('WHISPER: Audio duration is > 30s but chunking is not available. Will truncate!')

    time_end = datetime.datetime.now()
    infer_time = time_end - first_time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Loading audio took ' + str(infer_time_milliseconds) + ' ms')

    time_start = datetime.datetime.now()
    if use_chunking:
        chunks = []
        strides = []
        for chunk, stride in chunk_iter(audio):
            chunk = pad_or_trim(chunk)
            chunks.append(log_mel_spectrogram(chunk).numpy())
            strides.append(stride)
        mel_features = np.stack(chunks)
        batch_size = len(chunks)
    else:
        mel_audio = pad_or_trim(audio)
        mel_features = log_mel_spectrogram(mel_audio).numpy()
        # Ref Whisper returns shape (80, 3000) but model expects (1, 80, 3000)
        mel_features = np.expand_dims(mel_features, axis=0)
        batch_size = 1
    features = ctranslate2.StorageView.from_array(mel_features)

    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Feature extraction took ' + str(infer_time_milliseconds) + ' ms')

    # Whisper STEP 2 - optionally actually detect the language or default to configuration
    time_start = datetime.datetime.now()

    # System default language by default
    language = settings.language
    processor_language = f'<|{language}|>'

    if detect_language and not force_language:
        results = whisper_model.detect_language(features)
        language, probability = results[0][0]
        processor_language = language
        logger.debug(f"WHISPER: Detected language {language} with probability {probability}")

    else:
        if force_language:
            language = force_language
            logger.debug(f'WHISPER: Forcing language {language}')
            processor_language = f'<|{language}|>'
        else:
            logger.debug(f'WHISPER: Using system default language {language}')

    # Describe the task in the prompt.
    # See the prompt format in https://github.com/openai/whisper.
    prompt = models.whisper_processor.tokenizer.convert_tokens_to_ids(
        [
            "<|startoftranscript|>",
            processor_language,
            processor_task,
            "<|notimestamps|>",  # Remove this token to generate timestamps.
        ]
    )
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    if detect_language:
        logger.debug('WHISPER: Language detection took ' + str(infer_time_milliseconds) + ' ms')

    # Whisper STEP 3 - run model
    time_start = datetime.datetime.now()
    logger.debug(f'WHISPER: Using model {model} with beam size {beam_size}')
    results = whisper_model.generate(features, [prompt]*batch_size, beam_size=beam_size, return_scores=False)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Model took ' + str(infer_time_milliseconds) + ' ms')

    time_start = datetime.datetime.now()
    if use_chunking:
        assert strides, 'strides needed to compute final tokens when chunking'
        tokens = [(results[i].sequences_ids[0], strides[i]) for i in range(batch_size)]
        tokens = find_longest_common_sequence(tokens, models.whisper_processor.tokenizer)
    else:
        tokens = results[0].sequences_ids[0]
    results = models.whisper_processor.decode(tokens)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Decode took ' + str(infer_time_milliseconds) + ' ms')
    logger.debug('WHISPER: ASR transcript: ' + results)

    # Strip out token stuff
    pattern = re.compile("[A-Za-z0-9]+", )
    language = pattern.findall(language)[0]

    if translate:
        logger.debug(f'WHISPER: Detected non-preferred language {language}, translating')
        translation = do_translate(whisper_model, features, batch_size, language, beam_size=beam_size)
        # Strip tokens from translation output - brittle but works right now
        translation = translation.split('>')[2]
        translation = translation.strip()
        logger.debug(f'WHISPER: ASR translation: {translation}')
    else:
        translation = None

    # Strip tokens from results output - brittle but works right now
    # if detect_language:
    #     results = results.split('>')[2]
    # Remove trailing and leading spaces
    results = results.strip()

    time_end = datetime.datetime.now()
    infer_time = time_end - first_time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('WHISPER: Inference took ' + str(infer_time_milliseconds) + ' ms')
    infer_speedup = math.floor(audio_duration / infer_time_milliseconds)
    logger.debug('WHISPER: Inference speedup: ' + str(infer_speedup) + 'x')

    return language, results, infer_time_milliseconds, translation, infer_speedup, audio_duration


# Handy function for converting numbers to the individual word
def num_to_word(text):
    dct = {'0': 'zero', '1': 'one', '2': 'two', '3': 'three', '4': 'four',
           '5': 'five', '6': 'six', '7': 'seven', '8': 'eight', '9': 'nine'}
    newstr = ''
    for ch in text:
        if ch.isdigit() is True:
            dw = dct[ch]
            newstr = newstr+dw
        else:
            newstr = newstr+ch
    return newstr


def do_tts(text, format='WAV', speaker=tts_default_speaker):
    logger.debug(f'TTS: Got request for speaker {speaker} with text: {text}')

    if support_tts is False:
        return

    # Convert numbers to words because T5 doesn't support numbers
    if re.search(r'\d', text):
        logger.debug('TTS: Text contains numbers, converting to words')
        output_string = []
        for sentence in [text]:
            output_sentence = []
            for word in sentence.split():
                if word.isdigit():
                    word = num2words(word)
                    word = word.replace("-", " ")
                    output_sentence.append(word)
                else:
                    output_sentence.append(word)
            output_string.append(' '.join(output_sentence))
        text = str(output_string)
        logger.debug(f'TTS: Text after number substitution: {text}')

    # Load speaker embedding
    time_initial_start = datetime.datetime.now()

    file_path = f"wis/assets/spkemb/{speaker}.npy"
    if os.path.isfile(file_path):
        speaker_numpy = file_path
        logger.debug(f'TTS: Loaded included speaker {speaker}')

    # Try and potentially override with a custom speaker
    file_path = f"speakers/custom_tts/{speaker}.npy"
    if os.path.isfile(file_path):
        speaker_numpy = file_path
        logger.debug(f'TTS: Loaded custom speaker {speaker}')

    speaker_embedding = np.load(speaker_numpy)
    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0).to(device=device)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_initial_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('TTS: Loading speaker embedding took ' + str(infer_time_milliseconds) + ' ms')

    # Get inputs
    time_start = datetime.datetime.now()
    inputs = models.tts_processor(text=text, is_split_into_words=True, return_tensors="pt").to(device=device)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('TTS: Getting inputs took ' + str(infer_time_milliseconds) + ' ms')

    # Generate audio - SLOW
    time_start = datetime.datetime.now()
    with torch.inference_mode():
        audio = models.tts_model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=models.tts_vocoder).to(
            device=device)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('TTS: Generating audio took ' + str(infer_time_milliseconds) + ' ms')

    # Setup access to file and pass it back to calling function
    time_start = datetime.datetime.now()

    # If we're not running on CPU copy audio to CPU so we can write it out
    if device != "cpu":
        audio = audio.cpu()

    file = io.BytesIO()
    sf.write(file, audio.numpy(), samplerate=16000, format=format)
    file.seek(0)
    time_end = datetime.datetime.now()
    infer_time = time_end - time_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('TTS: Generating file took ' + str(infer_time_milliseconds) + ' ms')

    fake_filename = f'tts.{format}'
    media_type = mimetypes.guess_type(fake_filename)[0]

    time_end = datetime.datetime.now()
    infer_time = time_end - time_initial_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('TTS: Total time took ' + str(infer_time_milliseconds) + ' ms')

    return file, media_type


def do_sv(audio_file, threshold=sv_threshold):
    logger.debug(f'SV: Got request with threshold {threshold}')

    if sv_model is None:
        logger.warn('SV: Speaker verification support disabled')
        return

    time_initial_start = datetime.datetime.now()
    # Effects for processing of incoming audio
    sv_effects = [
        ["norm", "8"],
        ["trim", "0", "10"],
    ]
    # ["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
    # Use cosine simularity for comparison
    cosine_sim = torch.nn.CosineSimilarity(dim=-1)

    # Load audio from request
    audio, audio_sr = librosa.load(audio_file, sr=16000, mono=True)

    # Process incoming audio
    audio_wav, _ = apply_effects_tensor(
        torch.tensor(audio).unsqueeze(0), audio_sr, sv_effects)

    audio_input = sv_feature_extractor(audio_wav.squeeze(0), return_tensors="pt", sampling_rate=audio_sr
                                       ).input_values.to(device)

    with torch.inference_mode():
        audio_emb = sv_model(audio_input).embeddings
    audio_emb = torch.nn.functional.normalize(audio_emb, dim=-1).cpu()

    # Get all defined speakers
    emb_names = []
    emb_res = []
    examples = []

    emb_names.clear
    emb_res.clear
    examples.clear

    dir = "speakers/voice_auth"
    for f in os.listdir(dir):
        if (f.endswith(".npy")):
            name = re.sub(r'(.npy)$', '', f)
            examples.append(dir+f)
            file_path = f"{dir}/{name}.npy"
            if os.path.isfile(file_path):
                speaker_numpy = file_path
            emb = np.load(speaker_numpy)
            emb = torch.tensor(emb).unsqueeze(0).cpu()
            emb_res.append(emb)
            emb_names.append(name)
            logger.debug(f'SV: Loaded speaker {name}')

    result = {}
    for i in range(0, len(emb_names)):
        emb1 = emb_res[i]
        similarity = cosine_sim(emb1, audio_emb).numpy()[0]
        # result[emb_names[i]] = "{:.3f}".format(similarity)
        if similarity >= sv_threshold:
            result[emb_names[i]] = "{:.3f}".format(similarity)

    # Sort result from highest probability
    result = sorted(result.items(), key=operator.itemgetter(1), reverse=True)

    # Return dict
    result = dict(result)

    if result:
        result_string = str(result)
        logger.debug(f'SV: Got known speaker(s) {result_string}')
    else:
        logger.debug('SV: Unknown speaker')

    time_end = datetime.datetime.now()
    infer_time = time_end - time_initial_start
    infer_time_milliseconds = infer_time.total_seconds() * 1000
    logger.debug('SV: Total time took ' + str(infer_time_milliseconds) + ' ms')

    # Return result
    return result


# Adapted from https://github.com/thingless/t5voice
def do_speaker_embed(audio_file, speaker_name):
    spk_model = "speechbrain/spkrec-xvect-voxceleb"
    size_embed = 512
    # Override to CPU for now
    device = "cpu"

    tmpdir = tempfile.mkdtemp()
    try:
        classifier = EncoderClassifier.from_hparams(source=spk_model, run_opts={"device": device}, savedir=tmpdir)

        audio, sr = torchaudio.load(audio_file)
        if len(audio.shape) > 1 and audio.shape[0] > 1:
            audio = audio[0]  # left channel only
        if sr != 16000:
            # resample
            resampler = torchaudio.transforms.Resample(sr, 16000, dtype=audio.dtype)
            audio = resampler(audio)
        with torch.no_grad():
            embeddings = classifier.encode_batch(audio)
            embeddings = torch.nn.functional.normalize(embeddings, dim=2)
            save_path = f"speakers/custom_tts/{speaker_name}"
            np.save(save_path, embeddings.squeeze())
            embeddings = embeddings.squeeze().cpu().numpy()
        assert embeddings.shape[0] == size_embed, embeddings.shape[0]
    finally:
        assert len(tmpdir) > 3
        shutil.rmtree(tmpdir)

    return embeddings, save_path


class DataChannelMessage(NamedTuple):
    type: str
    message: Optional[str] = None
    obj: Optional[any] = None


def send_dc_response(channel, *args, **kargs):
    response = DataChannelMessage(*args, **kargs)
    channel.send(json.dumps(response._asdict()))


# Function for WebRTC handling
async def rtc_offer(request, model, beam_size, task, detect_language):
    params = await request.json()
    offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"])

    pc = RTCPeerConnection()
    pcs.add(pc)

    recorder = None
    top_track = None

    logger.debug(f'RTC: Created for {request.client.host}')

    @pc.on("datachannel")
    def on_datachannel(channel):
        @channel.on("message")
        def on_message(message):
            logger.debug("RTC DC: message: " + message)
            if not isinstance(message, str):
                logger.warn('RTC DC:  got non string message', message)
                return
            try:
                message = json.loads(message)
                message = DataChannelMessage(**message)
            except Exception:
                logger.exception("could not parse datachannel message", message)
                send_dc_response(channel, "error", "could not parse message")
                return
            if message.type == "ping":
                send_dc_response(channel, "pong", message.message)
            elif message.type == "start":
                # XXX what if top_track is still None i.e. we got start before we got track?
                logger.debug(f'RTC DC: Recording started with track {top_track}')
                nonlocal recorder
                recorder = MediaRecorderLite()
                recorder.addTrack(top_track)
                recorder.start()
                send_dc_response(channel, "log", "ASR Recording - start talking and press stop when done")
            elif message.type == "stop":
                logger.debug('RTC DC: Recording stopped')
                if not recorder:
                    send_dc_response(channel, "error", "Recording not yet started")
                    return
                obj = message.obj or {}
                model = obj.get('model') or settings.whisper_model_default
                beam_size = obj.get('beam_size') or settings.beam_size
                detect_language = obj.get('detect_language') or settings.detect_language
                logger.debug(f'RTC DC: Debug Stop Vars model {model} beam size {beam_size}'
                             f'detect language {detect_language}')
                logger.debug("RTC DC: Recording stopped")
                time_start_base = datetime.datetime.now()
                time_end = datetime.datetime.now()
                infer_time = time_end - time_start_base
                infer_time_milliseconds = infer_time.total_seconds() * 1000
                recorder.stop()
                logger.debug('RTC DC: Recorder stop took ' + str(infer_time_milliseconds) + ' ms')
                # Tell client what we are doing
                send_dc_response(channel, "log", f'Doing ASR with model {model} beam size {beam_size} '
                                 'detect language {detect_language} - please wait')
                # Finally call Whisper
                recorder.file.seek(0)
                language, results, infer_time, translation, infer_speedup, audio_duration = do_whisper(recorder.file,
                                                                                                       model, beam_size,
                                                                                                       task,
                                                                                                       detect_language)
                logger.debug("RTC DC: " + results)
                send_dc_response(channel, "infer", obj=dict(text=results))
                if translation:
                    send_dc_response(channel, "log", f'ASR Translation from {language}:  {translation}')
                infer_time = str(infer_time)
                audio_duration = str(audio_duration)
                infer_speedup = str(infer_speedup)
                send_dc_response(channel, "log", f'ASR Infer time: {infer_time} ms')
                send_dc_response(channel, "log", f'ASR Audio Duration: {audio_duration} ms')
                send_dc_response(channel, "log", f'ASR Speedup: {infer_speedup}x faster than realtime')
                recorder = None
            else:
                send_dc_response(channel, "error", f'unknown message type "{message.type}"')

    @pc.on("connectionstatechange")
    async def on_connectionstatechange():
        logger.debug(f'RTC: Connection state is {pc.connectionState}')
        if pc.connectionState == "failed" or pc.connectionState == "closed":
            try:
                await recorder.stop()
            except Exception:
                pass
            else:
                logger.debug("RTC: Recording stopped")
            await pc.close()
            pcs.discard(pc)
            # XXX: close recorders?
            logger.debug("RTC: Connection ended")

    @pc.on("track")
    def on_track(track):
        logger.debug(f'RTC: Track received {track.kind}')
        if track.kind == "audio":
            logger.debug("RTC: Setting top track")
            nonlocal top_track
            top_track = track

        @track.on("ended")
        async def on_ended():
            logger.debug(f'RTC: Track ended {track.kind}')

    # handle offer
    await pc.setRemoteDescription(offer)

    # send answer
    answer = await pc.createAnswer()
    await pc.setLocalDescription(answer)

    return {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type}

app = FastAPI(title=settings.name,
              description=settings.description,
              version=settings.version,
              openapi_url="/api/openapi.json",
              docs_url="/api/docs",
              redoc_url="/api/redoc")

if settings.cors_allowed_origins:
    app.add_middleware(
        CORSMiddleware,
        allow_origins=settings.cors_allowed_origins,
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

basic_auth_401_response = PlainTextResponse('Invalid credentials', status_code=401,
                                            headers={"WWW-Authenticate": "Basic"})


class HttpBasicAuth(BaseHTTPMiddleware):
    def __init__(self, app, username: str = None, password: str = None):
        super().__init__(app)
        self.username = username
        self.password = password

    async def dispatch(self, request: Request, call_next):
        if not request.headers.get('Authorization'):
            return basic_auth_401_response
        auth = request.headers["Authorization"]
        try:
            scheme, credentials = auth.split()
            if scheme.lower() != 'basic':
                return basic_auth_401_response
            decoded = base64.b64decode(credentials).decode("ascii")
        except (ValueError, UnicodeDecodeError, binascii.Error):
            return basic_auth_401_response
        username, _, password = decoded.partition(":")
        if self.username and username != self.username or self.password and password != self.password:
            return basic_auth_401_response
        return await call_next(request)


if settings.basic_auth_pass and settings.basic_auth_user:
    logger.info(f"{settings.name} is configured for HTTP Basic Authentication")
    app.add_middleware(HttpBasicAuth, username=settings.basic_auth_user, password=settings.basic_auth_pass)


@app.on_event("startup")
def startup_event():
    load_models()
    warm_models()
    logger.info(f"{settings.name} is ready for requests!")


@app.on_event("shutdown")
def shutdown_event():
    logger.info(f"{settings.name} is stopping (this can take a while)...")


# Mount static dir to serve files for WebRTC client
app.mount("/rtc", StaticFiles(directory="nginx/static/rtc", html=True), name="rtc_files")

# Mount static dir to serve files for dictation client
app.mount("/dict", StaticFiles(directory="nginx/static/dict", html=True), name="dict_files")

# Mount static dir to serve files for chatbot client
app.mount("/chatbot", StaticFiles(directory="nginx/static/chatbot", html=True), name="chatbot_files")

# Expose audio mount in the event willow is configured to save
app.mount("/audio", StaticFiles(directory="nginx/static/audio", html=True), name="audio_files")


class Ping(BaseModel):
    message: str


@app.get("/api/ping", response_model=Ping, summary="Ping for connectivity check", response_description="pong")
async def ping():
    response = {"message": "pong"}
    return JSONResponse(content=response)


@app.post("/api/rtc/asr", summary="Return SDP for WebRTC clients", response_description="SDP for WebRTC clients")
async def rtc_asr(request: Request, response: Response, model: Optional[str] = whisper_model_default,
                  task: Optional[str] = "transcribe", detect_language: Optional[bool] = detect_language,
                  beam_size: Optional[int] = beam_size):
    patch_loop_datagram()
    response = await rtc_offer(request, model, beam_size, task, detect_language)
    return JSONResponse(content=response)


class ASR(BaseModel):
    language: str
    results: str
    infer_time: float
    translation: Optional[str]
    infer_speedup: int
    audio_duration: int
    text: str


@app.post("/api/asr", response_model=ASR, summary="Submit audio file for ASR", response_description="ASR engine output")
async def asr(request: Request, audio_file: UploadFile, response: Response,
              model: Optional[str] = whisper_model_default, detect_language: Optional[bool] = detect_language,
              beam_size: Optional[int] = beam_size, force_language: Optional[str] = None,
              translate: Optional[bool] = False):
    # prof = profile.Profile()
    # prof.enable()
    logger.debug(f'FASTAPI: Got ASR request for model {model} beam size {beam_size} '
                 f'language detection {detect_language}')
    task = "transcribe"

    if force_language:
        if not check_language(force_language):
            logger.debug("FASTAPI: Invalid force_language in request - returning HTTP 400")
            response = {"error": "Invalid force_language"}
            return JSONResponse(content=response, status_code=status.HTTP_400_BAD_REQUEST)

    # Setup access to file
    audio_file = io.BytesIO(await audio_file.read())
    # Do Whisper
    language, results, infer_time, translation, infer_speedup, audio_duration = do_whisper(audio_file, model,
                                                                                           beam_size, task,
                                                                                           detect_language,
                                                                                           force_language, translate)

    # Create final response
    final_response = {"infer_time": infer_time, "infer_speedup": infer_speedup, "audio_duration": audio_duration,
                      "language": language, "text": results}

    # Handle translation in one response
    if translation:
        final_response['translation'] = translation

    # prof.disable()
    # print profiling output
    # stats = pstats.Stats(prof).strip_dirs().sort_stats("cumtime")
    # stats.print_stats(10) # top 10 rows
    return JSONResponse(content=final_response)


@app.post("/api/willow", response_model=ASR, summary="Stream Willow audio for ASR",
          response_description="ASR engine output")
async def willow(request: Request, response: Response, model: Optional[str] = whisper_model_default,
                 detect_language: Optional[bool] = detect_language, beam_size: Optional[int] = beam_size,
                 force_language: Optional[str] = None, translate: Optional[bool] = False,
                 save_audio: Optional[bool] = False, stats: Optional[bool] = False,
                 voice_auth: Optional[bool] = False):
    logger.debug(f'FASTAPI: Got WILLOW request for model {model} beam size {beam_size} '
                 f'language detection {detect_language}')
    task = "transcribe"

    if force_language:
        if not check_language(force_language):
            logger.debug("WILLOW: Invalid force_language in request - returning HTTP 400")
            response = {"error": "Invalid force_language"}
            return JSONResponse(content=response, status_code=status.HTTP_400_BAD_REQUEST)

    # Set defaults - use strings because we parse HTTP headers and convert to int later anyway
    sample_rate = "16000"
    bits = "16"
    channel = "1"
    codec = "pcm"

    body = b''
    sample_rate = request.headers.get('x-audio-sample-rate', '').lower()
    bits = request.headers.get('x-audio-bits', '').lower()
    channel = request.headers.get('x-audio-channel', '').lower()
    codec = request.headers.get('x-audio-codec', '').lower()
    willow_id = request.headers.get('x-willow-id', '').lower()

    logger.debug(f'WILLOW: Audio information: sample rate: {sample_rate}, bits: {bits}, channel(s): {channel}, '
                 f'codec: {codec}')

    if willow_id:
        logger.debug(f"WILLOW: Got Willow ID {willow_id}")

    async for chunk in request.stream():
        body += chunk

    try:
        if codec == "pcm":
            logger.debug("WILLOW: Source audio is raw PCM, creating WAV container")
            audio_file = write_stream_wav(body, int(sample_rate), int(bits), int(channel))
        elif codec == "wav":
            logger.debug("WILLOW: Source audio is wav")
            audio_file = io.BytesIO(body)
            audio_file.seek(0)
        else:
            logger.debug(f"WILLOW: Converting {codec} to wav")
            file = io.BytesIO(body)
            file.seek(0)
            audio_file = audio_to_wav(file, int(sample_rate))
    except Exception:
        logger.debug("WILLOW: Invalid audio in request - returning HTTP 400")
        response = {"error": "Invalid audio"}
        return JSONResponse(content=response, status_code=status.HTTP_400_BAD_REQUEST)

    # Save received audio if requested - defaults to false
    if save_audio:
        save_filename = 'nginx/static/audio/willow.wav'
        logger.debug(f"WILLOW: Saving audio to {save_filename}")
        with open(save_filename, 'wb') as f:
            f.write(audio_file.getbuffer())

    # Optionally authenticate voice
    if voice_auth:
        stats = True
        sv_results = do_sv(audio_file)
        if sv_results:
            logger.debug("WILLOW: Authenticated voice")
            # Seek back to 0 for Whisper later
            audio_file.seek(0)
            speaker = list(sv_results.keys())[0]
            speaker_status = (f"I heard {speaker} say:")
        else:
            logger.debug("WILLOW: Unknown or unauthorized voice - returning HTTP 406")
            return PlainTextResponse('Unauthorized voice', status_code=406)

    # Do Whisper
    language, results, infer_time, translation, infer_speedup, audio_duration = do_whisper(audio_file, model,
                                                                                           beam_size, task,
                                                                                           detect_language,
                                                                                           force_language, translate)

    # Create final response
    if stats:
        if voice_auth:
            final_response = {"infer_time": infer_time, "infer_speedup": infer_speedup,
                              "audio_duration": audio_duration, "language": language, "text": results,
                              "voice_auth": sv_results, "speaker_status": speaker_status}
        else:
            final_response = {"infer_time": infer_time, "infer_speedup": infer_speedup,
                              "audio_duration": audio_duration, "language": language, "text": results}
    else:
        final_response = {"language": language, "text": results}

    # Handle translation in one response
    if translation:
        final_response['translation'] = translation

    return JSONResponse(content=final_response)


if support_chatbot:
    @app.get("/api/chatbot", summary="Submit text for chatbot", response_description="Chatbot answer")
    async def chatbot(text: str, max_new_tokens: Optional[int] = chatbot_max_new_tokens,
                      temperature: Optional[float] = chatbot_temperature, top_p: Optional[float] = chatbot_top_p,
                      repetition_penalty: Optional[float] = chatbot_repetition_penalty):
        logger.debug(f"FASTAPI: Got chatbot request with text: {text}")
        # Do Chatbot
        response = do_chatbot(text, max_new_tokens, temperature, top_p, repetition_penalty)
        logger.debug(f"FASTAPI: Got chatbot response with text: {response}")
        final_response = {"response": response}
        return JSONResponse(content=final_response)

    @app.get("/api/chatbot/tts", summary="Submit text for chatbot and get audio in response",
             response_description="Chatbot answer audio")
    async def chatbot_tts(text: str, max_new_tokens: Optional[int] = chatbot_max_new_tokens,
                          temperature: Optional[float] = chatbot_temperature, top_p: Optional[float] = chatbot_top_p,
                          repetition_penalty: Optional[float] = chatbot_repetition_penalty,
                          format: Optional[str] = tts_default_format,
                          speaker: Optional[str] = tts_default_speaker):
        logger.debug(f"FASTAPI: Got chatbot TTS request with text: {text} and speaker {speaker}")
        # Do Chatbot
        chatbot = do_chatbot(text, max_new_tokens, temperature, top_p, repetition_penalty)
        logger.debug(f"FASTAPI: Got chatbot TTS response with text: {chatbot}")
        # Do TTS
        response, media_type = do_tts(chatbot, format, speaker)
        return StreamingResponse(response, media_type=media_type)

if support_tts:
    @app.get("/api/tts", summary="Submit text for text to speech",
             response_description="Audio file of generated speech")
    async def tts(text: str, format: Optional[str] = tts_default_format, speaker: Optional[str] = tts_default_speaker):
        logger.debug(f"FASTAPI: Got TTS request for speaker {speaker} with format {format} and text: {text}")
        # Do TTS
        response, media_type = do_tts(text, format, speaker)

        return StreamingResponse(response, media_type=media_type)

    @app.post("/api/sts", summary="Submit speech, do ASR, and TTS",
              response_description="Audio file of generated speech")
    async def sts(request: Request, audio_file: UploadFile, response: Response,
                  model: Optional[str] = whisper_model_default, detect_language: Optional[bool] = detect_language,
                  beam_size: Optional[int] = beam_size, force_language: Optional[str] = None,
                  translate: Optional[bool] = False, speaker: Optional[str] = tts_default_speaker):
        logger.debug(f'FASTAPI: Got STS request for model {model} beam size {beam_size} '
                     f'language detection {detect_language}')
        task = "transcribe"

        if force_language:
            if not check_language(force_language):
                logger.debug("FASTAPI: Invalid force_language in request - returning HTTP 400")
                response = {"error": "Invalid force_language"}
                return JSONResponse(content=response, status_code=status.HTTP_400_BAD_REQUEST)

        # Setup access to file
        audio_file = io.BytesIO(await audio_file.read())
        # Do Whisper
        language, results, infer_time, translation, infer_speedup, audio_duration = do_whisper(audio_file, model,
                                                                                               beam_size, task,
                                                                                               detect_language,
                                                                                               force_language,
                                                                                               translate)

        # Do TTS
        response = do_tts(results, 'FLAC', speaker)
        fake_filename = f'tts.{format}'
        media_type = mimetypes.types_map[fake_filename]
        return StreamingResponse(response, media_type=media_type[0])

    class Speaker(BaseModel):
        message: str

    @app.post("/api/speaker", response_model=Speaker, summary="Add custom speaker",
              response_description="Speaker creation status")
    async def speaker_create(request: Request, audio_file: UploadFile, speaker_name: Optional[str] = "CUSTOM"):
        logger.debug("FASTAPI: Got new speaker request")
        # Setup access to file
        audio_file = io.BytesIO(await audio_file.read())
        # Do embed but don't do anything with the output other than save in do_speaker_embed
        embedding, save_path = do_speaker_embed(audio_file, speaker_name)
        status_text = f"Created custom speaker successfully - you can now use the {speaker_name} speaker for TTS"
        logger.debug(f"FASTAPI: {status_text}")

        response = {"message": status_text}
        return JSONResponse(content=response)

    @app.delete("/api/speaker", response_model=Speaker, summary="Delete custom speaker",
                response_description="Speaker deletion status")
    async def speaker_delete(request: Request, speaker_name: Optional[str] = "CUSTOM"):
        logger.debug("FASTAPI: Got delete speaker request")
        os.remove(f'speakers/custom_tts/{speaker_name}.npy')
        status_text = f"Deleted custom speaker {speaker_name} successfully"
        logger.debug(f"FASTAPI: {status_text}")

        response = {"message": status_text}
        return JSONResponse(content=response)

    class SpeakersList(BaseModel):
        speakers: list

    @app.get("/api/speaker", response_model=SpeakersList, summary="Show supported speakers",
             response_description="Speakers list")
    async def speaker_delete_get(request: Request):
        logger.debug("FASTAPI: Got list speakers request")
        speakers = []
        dirs = ["wis/assets/spkemb", "speakers/custom_tts"]
        for dir in dirs:
            logger.debug(f"FASTAPI: Getting speakers for directory {dir}")
            for (root, dirs, file) in os.walk(dir):
                for f in file:
                    if '.npy' in f:
                        name = f.replace(".npy", "")
                        logger.debug(f"FASTAPI: Getting speakers found speaker {name}")
                        speakers.append(name)

        response = {"speakers": speakers}
        return JSONResponse(content=response)


class ConnectionManager:
    def __init__(self):
        self.active_connections: List[WebSocket] = []

    async def connect(self, websocket: WebSocket):
        await websocket.accept()
        self.active_connections.append(websocket)

    def disconnect(self, websocket: WebSocket):
        self.active_connections.remove(websocket)


manager = ConnectionManager()
if support_chatbot:
    @app.websocket("/api/ws/chatbot")
    async def websocket_chatbot(websocket: WebSocket):
        await manager.connect(websocket)
        try:
            while True:
                text = await websocket.receive_text()
                # await websocket.send_text(f'Asking chatbot {text}')
                output = do_chatbot(text)
                await websocket.send_text(output)
        except WebSocketDisconnect:
            manager.disconnect(websocket)