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https://github.com/huggingface/speech-to-speech

Speech To Speech: an effort for an open-sourced and modular GPT4-o
https://github.com/huggingface/speech-to-speech

ai assistant language-model machine-learning python speech speech-synthesis speech-to-text speech-translation

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Speech To Speech: an effort for an open-sourced and modular GPT4-o

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# Speech To Speech: an effort for an open-sourced and modular GPT4-o



## 📖 Quick Index

* [Approach](#approach)

  - [Structure](#structure)

  - [Modularity](#modularity)

* [Setup](#setup)

* [Usage](#usage)

  - [Docker Server approach](#docker-server)

  - [Server/Client approach](#serverclient-approach)

  - [Local approach](#local-approach-running-on-mac)

* [Command-line usage](#command-line-usage)

  - [Model parameters](#model-parameters)

  - [Generation parameters](#generation-parameters)

  - [Notable parameters](#notable-parameters)



## Approach



### Structure

This repository implements a speech-to-speech cascaded pipeline with consecutive parts:

1. **Voice Activity Detection (VAD)**: [silero VAD v5](https://github.com/snakers4/silero-vad)

2. **Speech to Text (STT)**: Whisper checkpoints (including [distilled versions](https://huggingface.co/distil-whisper))

3. **Language Model (LM)**: Any instruct model available on the [Hugging Face Hub](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending)! 🤗

4. **Text to Speech (TTS)**: [Parler-TTS](https://github.com/huggingface/parler-tts)🤗



### Modularity

The pipeline aims to provide a fully open and modular approach, leveraging models available on the Transformers library via the Hugging Face hub. The level of modularity intended for each part is as follows:

- **VAD**: Uses the implementation from [Silero's repo](https://github.com/snakers4/silero-vad).

- **STT**: Uses Whisper models exclusively; however, any Whisper checkpoint can be used, enabling options like [Distil-Whisper](https://huggingface.co/distil-whisper/distil-large-v3) and [French Distil-Whisper](https://huggingface.co/eustlb/distil-large-v3-fr).

- **LM**: This part is fully modular and can be changed by simply modifying the Hugging Face hub model ID. Users need to select an instruct model since the usage here involves interacting with it.

- **TTS**: The mini architecture of Parler-TTS is standard, but different checkpoints, including fine-tuned multilingual checkpoints, can be used.



The code is designed to facilitate easy modification. Each component is implemented as a class and can be re-implemented to match specific needs.



## Setup



Clone the repository:

```bash

git clone https://github.com/huggingface/speech-to-speech.git

cd speech-to-speech

```



Install the required dependencies using [uv](https://github.com/astral-sh/uv):

```bash

uv pip install -r requirements.txt

```



For Mac users, use the `requirements_mac.txt` file instead:

```bash

uv pip install -r requirements_mac.txt

```



If you want to use Melo TTS, you also need to run:

```bash

python -m unidic download

```



## Usage



The pipeline can be run in two ways:

- **Server/Client approach**: Models run on a server, and audio input/output are streamed from a client.

- **Local approach**: Runs locally.



### Docker Server



#### Install the NVIDIA Container Toolkit



https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html



#### Start the docker container

```docker compose up```



### Server/Client Approach



To run the pipeline on the server:

```bash

python s2s_pipeline.py --recv_host 0.0.0.0 --send_host 0.0.0.0

```



Then run the client locally to handle sending microphone input and receiving generated audio:

```bash

python listen_and_play.py --host 

```



### Local approach (running on Mac)

To run on mac, we recommend setting the flag `--local_mac_optimal_settings`:

```bash

python s2s_pipeline.py --local_mac_optimal_settings

```



You can also pass `--device mps` to have all the models set to device mps.

The local mac optimal settings set the mode to be local as explained above and change the models to:

- LightningWhisperMLX

- MLX LM

- MeloTTS



### Recommended usage with Cuda



Leverage Torch Compile for Whisper and Parler-TTS:



```bash

python s2s_pipeline.py \

	--recv_host 0.0.0.0 \

	--send_host 0.0.0.0 \

	--lm_model_name microsoft/Phi-3-mini-4k-instruct \

	--init_chat_role system \

	--stt_compile_mode reduce-overhead \

	--tts_compile_mode default 

```



For the moment, modes capturing CUDA Graphs are not compatible with streaming Parler-TTS (`reduce-overhead`, `max-autotune`).



## Command-line Usage



### Model Parameters



`model_name`, `torch_dtype`, and `device` are exposed for each part leveraging the Transformers' implementations: Speech to Text, Language Model, and Text to Speech. Specify the targeted pipeline part with the corresponding prefix:

- `stt` (Speech to Text)

- `lm` (Language Model)

- `tts` (Text to Speech)



For example:

```bash

--lm_model_name google/gemma-2b-it

```



### Generation Parameters



Other generation parameters of the model's generate method can be set using the part's prefix + `_gen_`, e.g., `--stt_gen_max_new_tokens 128`. These parameters can be added to the pipeline part's arguments class if not already exposed (see `LanguageModelHandlerArguments` for example).



### Notable Parameters



#### VAD Parameters

- `--thresh`: Threshold value to trigger voice activity detection.

- `--min_speech_ms`: Minimum duration of detected voice activity to be considered speech.

- `--min_silence_ms`: Minimum length of silence intervals for segmenting speech, balancing sentence cutting and latency reduction.



#### Language Model

- `--init_chat_role`: Defaults to `None`. Sets the initial role in the chat template, if applicable. Refer to the model's card to set this value (e.g. for [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) you have to set `--init_chat_role system`)

- `--init_chat_prompt`: Defaults to `"You are a helpful AI assistant."` Required when setting `--init_chat_role`.



#### Speech to Text

- `--description`: Sets the description for Parler-TTS generated voice. Defaults to: `"A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."`



- `--play_steps_s`: Specifies the duration of the first chunk sent during streaming output from Parler-TTS, impacting readiness and decoding steps.



## Citations



### Silero VAD

```bibtex

@misc{Silero VAD,

  author = {Silero Team},

  title = {Silero VAD: pre-trained enterprise-grade Voice Activity Detector (VAD), Number Detector and Language Classifier},

  year = {2021},

  publisher = {GitHub},

  journal = {GitHub repository},

  howpublished = {\url{https://github.com/snakers4/silero-vad}},

  commit = {insert_some_commit_here},

  email = {[email protected]}

}

```



### Distil-Whisper

```bibtex

@misc{gandhi2023distilwhisper,

      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling},

      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},

      year={2023},

      eprint={2311.00430},

      archivePrefix={arXiv},

      primaryClass={cs.CL}

}

```



### Parler-TTS

```bibtex

@misc{lacombe-etal-2024-parler-tts,

  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},

  title = {Parler-TTS},

  year = {2024},

  publisher = {GitHub},

  journal = {GitHub repository},

  howpublished = {\url{https://github.com/huggingface/parler-tts}}

}

```