echodict/RWKV-ASR

Speech missions with frozen RWKV language models

• 中文说明
• English

This repo is an exploratory experiment to enable frozen pretrained RWKV language models to accept speech modality input. Generally, LLMs trained on text data are not directly applicable to speech recognition tasks, and there are many solutions (such as adapters + pretrained audio encoders, or neural audio codecs) to bridge the gap between text and speech. We followed the idea of SLAM_ASR and used the RWKV language model as the LLM, and instead of directly writing a prompt template we directly finetuned the initial state of the RWKV model. We were able to achieve 4.6% WER on Librispeech 960h Clean test set (6.9% on Other test) with a 3B RWKV model.

This code inside is developed on RWKV-PEFT. And the current implementation of speech encoder and adapter is based on SLAM_ASR.

Roadmap

We want to explore compute-efficient and high-performance ways to extend text-based RWKV into multimodal ones. In the audio and speech modality, these are the tasks we are attempting:

• ASR in single language
• ASR in many languages
• Speech Translation
• Voice input question answering (like GPT-4o)
• Other audio missions
• Multiple turns answering

Environment

The following command will create a new conda environment and install the required packages:

conda create -n rwkv python=3.10
conda activate rwkv
pip install -r requirements.txt

Training

1. Download RWKV-6-World model files from one of the following links. We used the 3B model in our experiments, i.e. RWKV-x060-World-3B-v2.1-20240417-ctx4096.pth.

• Hugging Face
• Hf Mirror (CN)
• Modelscope

2. Open demo/demo-state-tuning.sh. Set OP=train for training and load_model=path/to/your/model/. Modify n_layer and n_embd according to the table below:

Model	n_layer	n_embd
1.6B	24	2048
3B	32	2560
7B	32	4096
14B	61	4096

Other parameters for training:

parameter	description
micro_bsz	batch size for each device
epoch_steps	num of steps in 1 epoch. please modified as (dataset size / real batch size)
device	num of GPU for training

The default setting will train a 3B rwkv model on librispeech 960h dataset, with 4 devices and a batch size of 4 per device (real batch size = 16).

3. The script will overwrite the .pth file in output/. Make sure to save the needed .pth model files under this path to other dir before the training.
4. run sh demo/demo-state-tuning.sh to start the training process.

The training process looks like this:

• It first loads the provided RWKV model, and a speech encoder model from huggingface. An adapter and an initial state for RWKV model will be initialized randomly.
• The (symbolically) simplified formula for this model is:

RWKV( [InitialState], [Adapter](SpeechEncoder(audio))) -> "The weather is good. <s>"

Modules and variables in [ ] will be trained, the rest is all frozen.

There are also some codes to enable other PEFT training of the whole model. Note that not all methods are fully adapted to speech modality training as of now, and we are still actively working on this.

Evaluation

Follow the instruction in Training, but modify OP=eval in demo/demo-state-tuning.sh. The trained model in output/ will be used to calculate the WER of the model in output/ on the clean test set and the other test set of Librispeech.

Audio File Prediction

Open demo/demo-predict.sh and modify file_path=path/to/your/audio/file. Run sh demo/demo-predict.sh to load trained weights in output/ and predict the content of the input audio file.

Pretrained weights

Download the pretrained weights from the following link:

ASR:https://huggingface.co/JerryAGENDD/RWKV-ASR/tree/main/ASR

SpeechTranslate:https://huggingface.co/JerryAGENDD/RWKV-ASR/tree/main/ST

SpeechQA:https://huggingface.co/JerryAGENDD/RWKV-ASR/tree/main/SpeechQA

The pretrained weights contain the necessary parameters for the adapter and the RWKV initial state. These weights are trained using WavLM Large as the speech encoder and RWKV-3B as the language model (script default configuration). Place the weights in the output/ directory for the script to load them.

Speech Chat with RWKV

A script for real-time speech conversation with RWKV:

https://github.com/AGENDD/RWKV-SpeechChat

You can use the trained weights to interact with RWKV in real time.