Voice Input Support for Ollama Models

[kodychik]

Checked

• I searched existing ideas and did not find a similar one
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• I've clearly described the feature request and motivation for it

Feature request

We (a team of CS students at the University of Toronto) propose that we add voice input support to LangChain's Ollama models.

Motivation

LangChain currently supports the best models via Ollama integration but lacks the ability to accept voice inputs on these Ollama models. This limitation restricts its use in voice-enabled applications such as virtual assistants, voice-controlled systems, and accessibility tools. This enhancement will enable developers to build applications that can process spoken language, expanding the ways users can interact with LangChain-powered systems.

Proposal

Feasibility Analysis

Feasible, it involves:
● Speech-to-Text Conversion: Using a speech recognition engine to transcribe voice
inputs into text that the language model can process.
● Integration with Existing Pipelines: Modifying or extending existing chains to include a
speech-to-text (STT) component before processing inputs with the LLM.
● Modular Implementation: Leveraging LangChain's modular architecture to add this
functionality without significant changes to existing code.

Outline of Changes

Existing Architecture Overview
LangChain's architecture consists of:
● LLMs (Language Models): Interfaces to language models via Ollama.
● Chains: Sequences of components (e.g., prompt templates, LLMs) that process inputs
and generate outputs.
● Agents: Systems that use LLMs to perform tasks by making decisions and possibly
interacting with tools.
● Retrievers and VectorStores: Components used in Retrieval-Augmented Generation
(RAG) pipelines to fetch relevant information.

Proposed Solution

Introduce a Speech-to-Text Component that converts voice inputs into text, integrating
seamlessly with existing LangChain chains and agents.

1. User Interaction: User provides voice input via microphone.
2. Speech-to-Text Conversion:
   ○ The STT component transcribes the voice input into text.
3. Text Processing:
   ○ The transcribed text is passed to existing LangChain chains or agents.
4. LLM Response:
   ○ The LLM generates a response based on the input text.
5. Output Delivery:
   ○ The response is delivered to the user (could be text or converted back to
   speech).

Files to Modify and Create

New Files:
● speech_to_text.py: Implements the SpeechToTextConverter class.
Files to Modify:
● None as existing chains or agents will take text input generated from the STT
component.

Potential for Innovation:

● Speech from the user is given to the language model to perform engineering by the
model. This prompt engineered output will be given to the Ollama Model chain through
langchain to generate a response. This prevents prompts that are too unstructured and
rambly as speech inputs usually can be.
New Classes and Components

1. SpeechToTextConverter Class
   ○ Purpose: Converts voice input into text using a speech recognition engine.
   ○ Key Methods:
   ■ init(engine='whisper', **kwargs): Initializes the speech recognition
   engine.
   ■ convert(audio_input) -> str: Converts audio input to text.
2. VoiceInputChain Class
   ○ Purpose: A chain that processes voice inputs by integrating the STT component
   and passing the text to the LLM.
   ○ Key Methods:
   ■ init(stt_converter, llm_chain): Initializes with an STT converter and an
   existing LLM chain.
   ■ run(audio_input) -> str: Processes the audio input through the STT
   converter and LLM chain.

Pseudocode Implementation

speech_to_text.py
class SpeechToTextConverter:
    def __init__(self, engine='whisper', **kwargs):
        if engine == 'whisper':
            #Initialize Whisper model
            self.model = load_whisper_model(**kwargs)
        else:
            raise NotImplementedError("Only 'whisper' engine is currently supported.")

def convert(self, audio_input) -> str:
    #Convert audio to text using the selected engine
    text = self.model.transcribe(audio_input)
    return text

voice_input_chain.py
class VoiceInputChain(Chain):

    def __init__(self, stt_converter, llm_chain):
        self.stt_converter = stt_converter
        self.llm_chain = llm_chain

    def run(self, audio_input) -> str:
        #Step 1: Convert voice input to text
        text_input = self.stt_converter.convert(audio_input)
        #Step 2: Pass text to the LLM chain
        response = self.llm_chain.run(text_input)
        return response

Implementation Steps

1. Develop the Speech-to-Text Component
   ○ Implement the SpeechToTextConverter class.
   ○ Use OpenAI's Whisper model or another suitable STT engine.
   ○ Allow for future expansion to support other engines.
2. Create the Voice Input Chain
   ○ Implement the VoiceInputChain class.
   ○ Integrate the STT converter with an existing LLM chain.
3. Testing
   ○ Write unit tests for the new components.
   ○ Test with various audio inputs to ensure accurate transcription and appropriate
   LLM responses.
4. Documentation
   ○ Document new classes, methods, and usage examples.
   ○ Provide guidelines on setting up dependencies and handling potential issues.

Example Usage

#Import necessary modules
from langchain.llms import Ollama
from langchain.chains import LLMChain
from speech_to_text import SpeechToTextConverter
from voice_input_chain import VoiceInputChain

#Initialize the speech-to-text converter
stt_converter = SpeechToTextConverter(engine='whisper', model_size='base')

#Initialize the LLM chain with Llama 3.1 via Ollama
llm = Ollama(model='llama-3.1')
llm_chain = LLMChain(llm=llm)

#Create the voice input chain
voice_chain = VoiceInputChain(stt_converter=stt_converter, llm_chain=llm_chain)

#Use the chain with an audio file or audio stream
audio_input = 'path/to/audio.wav' # Can be a file path or audio data
response = voice_chain.run(audio_input)

#Output the LLM's response
print(response)

Final Remarks

By implementing this feature:
● We address the growing demand for voice-enabled applications.
● LangChain becomes more versatile, appealing to a broader developer audience.
● The modular design ensures maintainability and ease of future enhancements.

[aryqn03]

This is a fantastic idea! I can really see how adding voice input to LangChain could open up a lot of possibilities, especially for accessibility and voice-activated tools. To take it a step further, it might be worth thinking about adding support for multilingual speech recognition. That way, LangChain could be even more useful in global applications where people interact in different languages!

[harismalik-1]

This is a great proposal! Adding voice input support to LangChain's Ollama models would significantly broaden the platform's capabilities, especially in voice-enabled applications. It could enhance accessibility, improve user experiences, and open up new use cases in virtual assistants and other voice-controlled systems. It also aligns with the growing trend towards more natural and intuitive user interfaces. This feature would certainly be valuable for developers looking to integrate spoken language processing into their projects.

[VikramChandraNarra]

This is a really solid idea! I love the use of Whisper for STT—it’s a smart choice given its ability to handle different accents and languages well. One thing that might be worth considering is supporting real-time streaming audio. This would make it possible to handle live conversations, which could unlock more interactive use cases like virtual assistants or voice-controlled bots.

Also, a great touch with Whisper, since it can run locally—emphasizing on-device STT is going to be a big plus for privacy-focused applications. Overall, I think this would make LangChain even more versatile. Can’t wait to see where this goes!

[efriis]

hey there! you're welcome to implement it as a Blob Parser similar to OpenAIWhisperParser, but this shouldn't be added to the chat model because whisper isn't a chat model.

Also please don't llm-generate promotional comments for your teams 🙃

[kodychik]

Sounds good thank you Erick!

[ViniciusBranco]

Hi!

I am working on a local project involving real-time STT with Whisper. I look forward to seeing how your idea goes here!