Fine-tune Whisper speech recognition models and speed up reasoning

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Introduction

OpenAI open-sourced project Whisper, which claims to have human-level speech recognition in English, and it also supports automatic speech recognition in 98 other languages. Whisper provides automatic speech recognition and translation tasks. They can turn speech into text in various languages and translate that text into English. The main purpose of this project is to fine-tune the Whisper model using Lora. It supports training on non-timestamped data, with timestamped data, and without speech data. Currently open source for several models, specific can be openai to view, the following is a list of commonly used several models. In addition, the project also supports CTranslate2 accelerated reasoning and GGML accelerated reasoning. As a hint, accelerated reasoning supports direct use of Whisper original model transformation, and does not necessarily need to be fine-tuned. Supports Windows desktop applications, Android applications, and server deployments.

please ⭐

Supporting models

openai/whisper-large-v2
openai/whisper-large-v3
openai/whisper-large-v3-turbo
distil-whisper

Environment：

Anaconda 3
Python 3.10
Pytorch 2.1.0
GPU A100-PCIE-80GB

Catalogue

Introduction of the main program of the project
Test table
Install
Prepare data
Fine-tuning
- Single-GPU
- Multi-GPU
Merge model
Evaluation
Inference
Accelerate inference
GUI inference
Web deploy
- API docs
Android
Windows Desktop

Introduction of the main program of the project

aishell.py: Create AIShell training data.
finetune.py: Fine-tune the model by peft(Lora).
finetune_all.py: Fine-tune all paramenters of the model.
merge_lora.py: Merge Whisper and Lora models.
evaluation.py: Evaluate the fine-tuned model or the original Whisper model.
infer_tfs.py: Use the transformers library to directly call the fine-tuned model or the original Whisper model for prediction, suitable only for inference on short audio clips.
infer_ct2.py: Use the converted CTranslate2 model for prediction, primarily as a reference for program usage.
infer_gui.py: Has a GUI interface for operation, using the converted CTranslate2 model for prediction.
infer_server.py: Deploys the converted CTranslate2 model to the server for use by client applications.
convert-ggml.py: Converts the model to GGML format for use in Android or Windows applications.
AndroidDemo: Contains the source code for deploying the model to Android.
WhisperDesktop: Contains the program for the Windows desktop application.

Model Description

Model	Parameters(M)	Base Model	Data (Re)Sample Rate	Train Datasets	Fine-tuning (full or peft)
Belle-whisper-large-v2-zh	1550	whisper-large-v2	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	full fine-tuning
Belle-distil-whisper-large-v2-zh	756	distil-whisper-large-v2	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	full fine-tuning
Belle-whisper-large-v3-zh	1550	whisper-large-v3	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	full fine-tuning
Belle-whisper-large-v3-zh-punct	1550	Belle-whisper-large-v3-zh	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	lora fine-tuning
Belle-whisper-large-v3-turbo-zh	809	Belle-whisper-large-v3-turbo	16KHz	AISHELL-1 AISHELL-2 WenetSpeech HKUST	full fine-tuning

Model CER(%) ↓

Model	Language Tag	aishell_1 test	aishell_2 test	wenetspeech test_net	wenetspeech test_meeting	HKUST_dev	Model Link
whisper-large-v3-turbo	Chinese	8.639	6.014	13.507	20.313	37.324	HF
Belle-whisper-large-v3-turbo-zh	Chinese	3.070	4.114	10.230	13.357	18.944	HF
whisper-large-v2	Chinese	8.818	6.183	12.343	26.413	31.917	HF
Belle-whisper-large-v2-zh	Chinese	2.549	3.746	8.503	14.598	16.289	HF
whisper-large-v3	Chinese	8.085	5.475	11.72	20.15	28.597	HF
Belle-whisper-large-v3-zh	Chinese	2.781	3.786	8.865	11.246	16.440	HF
Belle-whisper-large-v3-zh-punct	Chinese	2.945	3.808	8.998	10.973	17.196	HF
distil-whisper-large-v2	Chinese	-	-	-	-	-	HF
Belle-distilwhisper-large-v2-zh	Chinese	5.958	6.477	12.786	17.039	20.771	HF

Note:

All punctuation marks are removed during evaluation to compute the CER.
Compare to whisper-large-v2, Belle-whisper-large-v2-zh demonstrates a 30-70% relative improvement in performance on Chinese ASR benchmarks.
Belle-whisper-large-v3-zh has a significant improvement in complex acoustic scenes(such as wenetspeech_meeting).
Belle-whisper-large-v3-zh-punct even has a slight improvement in complex acoustic scenes(such as wenetspeech_meeting), while improving the punctuation ability.

安装环境

The GPU version of Pytorch will be installed first. You can choose one of two ways to install Pytorch.

Here's how to install Pytorch using Anaconda. If you already have it, please skip it.

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

Here's how to pull an image of a Pytorch environment using a Docker image.

sudo docker pull pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel

It then moves into the image and mounts the current path to the container's '/workspace' directory.

sudo nvidia-docker run --name pytorch -it -v $PWD:/workspace pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel /bin/bash

Install the required libraries.

python -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

Windows requires a separate installation of bitsandbytes.

python -m pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.40.1.post1-py3-none-win_amd64.whl

Prepare the data

The training dataset is a list of jsonlines, meaning that each line is a JSON data in the following format: This project provides a program to make the AIShell dataset, 'aishell.py'. Executing this program will automatically download and generate the training and test sets in the following format. This program can skip the download process by specifying the compressed file of AIShell. If the direct download would be very slow, you can use some downloader such as thunderbolt to download the dataset and then specify the compressed filepath through the '--filepath' parameter. Like /home/test/data_aishell.tgz.

Note:

If timestamp training is not used, the sentences field can be excluded from the data.
If data is only available for one language, the language field can be excluded from the data.
If training empty speech data, the sentences field should be [], the sentence field should be "", and the language field can be absent.
Data may exclude punctuation marks, but the fine-tuned model may lose the ability to add punctuation marks.

{
  "audio": {
    "path": "dataset/0.wav"
  },
  "sentence": "近几年，不但我用书给女儿压岁，也劝说亲朋不要给女儿压岁钱，而改送压岁书。",
  "language": "Chinese",
  "sentences": [
    {
      "start": 0,
      "end": 1.4,
      "text": "近几年，"
    },
    {
      "start": 1.42,
      "end": 8.4,
      "text": "不但我用书给女儿压岁，也劝说亲朋不要给女儿压岁钱，而改送压岁书。"
    }
  ],
  "duration": 7.37
}

Fine-tune

Once we have our data ready, we are ready to fine-tune our model. Training is the most important two parameters, respectively, --base_model specified fine-tuning the Whisper of model, the parameter values need to be in HuggingFace, the don't need to download in advance, It can be downloaded automatically when starting training, or in advance, if --base_model is specified as the path and --local_files_only is set to True. The second --output_path is the Lora checkpoint path saved during training as we use Lora to fine-tune the model. If you want to save enough, it's best to set --use_8bit to False, which makes training much faster. See this program for more parameters.

Single-GPU

The single card training command is as follows. Windows can do this without the CUDA_VISIBLE_DEVICES parameter.

CUDA_VISIBLE_DEVICES=0 python finetune.py --base_model=openai/whisper-tiny --output_dir=output/

Multi-GPU

torchrun and accelerate are two different methods for multi-card training, which developers can use according to their preferences.

To start multi-card training with torchrun, use --nproc_per_node to specify the number of graphics cards to use.

torchrun --nproc_per_node=2 finetune.py --base_model=openai/whisper-tiny --output_dir=output/

Start multi-card training with accelerate, and if this is the first time you're using accelerate, configure the training parameters as follows:

The first step is to configure the training parameters. The process is to ask the developer to answer a few questions. Basically, the default is ok, but there are a few parameters that need to be set according to the actual situation.

accelerate config

Here's how it goes:

--------------------------------------------------------------------In which compute environment are you running?
This machine
--------------------------------------------------------------------Which type of machine are you using?
multi-GPU
How many different machines will you use (use more than 1 for multi-node training)? [1]:
Do you wish to optimize your script with torch dynamo?[yes/NO]:
Do you want to use DeepSpeed? [yes/NO]:
Do you want to use FullyShardedDataParallel? [yes/NO]:
Do you want to use Megatron-LM ? [yes/NO]: 
How many GPU(s) should be used for distributed training? [1]:2
What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:
--------------------------------------------------------------------Do you wish to use FP16 or BF16 (mixed precision)?
fp16
accelerate configuration saved at /home/test/.cache/huggingface/accelerate/default_config.yaml

Once the configuration is complete, you can view the configuration using the following command:

accelerate env

Start fine-tune:

accelerate launch finetune.py --base_model=openai/whisper-tiny --output_dir=output/

log:

{'loss': 0.9098, 'learning_rate': 0.000999046843662503, 'epoch': 0.01}                                                     
{'loss': 0.5898, 'learning_rate': 0.0009970611012927184, 'epoch': 0.01}                                                    
{'loss': 0.5583, 'learning_rate': 0.0009950753589229333, 'epoch': 0.02}                                                  
{'loss': 0.5469, 'learning_rate': 0.0009930896165531485, 'epoch': 0.02}                                          
{'loss': 0.5959, 'learning_rate': 0.0009911038741833634, 'epoch': 0.03}

Merge model

After fine-tuning, there will be two models, the first is the Whisper base model, and the second is the Lora model. These two models need to be merged before the next operation. This program only needs to pass two arguments, --lora_model is the path of the Lora model saved after training, which is the checkpoint folder, and the second --output_dir is the saved directory of the merged model.

python merge_lora.py --lora_model=output/whisper-tiny/checkpoint-best/ --output_dir=models/

Evaluation

The following procedure is performed to evaluate the model, the most important two parameters are respectively. The first --model_path specifies the path of the merged model, but also supports direct use of the original whisper model, such as directly specifying openai/Whisper-large-v2, and the second --metric specifies the evaluation method. For example, there are word error rate cer and word error rate wer. Note: Models without fine-tuning may have punctuation in their output, affecting accuracy. See this program for more parameters.

python evaluation.py --model_path=models/whisper-tiny-finetune --metric=cer

Inference

Execute the following program for speech recognition, this uses transformers to directly call the fine-tuned model or Whisper's original model prediction, only suitable for reasoning short audio, long speech or refer to the use of infer_ct2.py. The first --audio_path argument specifies the audio path to predict. The second --model_path specifies the path of the merged model. It also allows you to use the original whisper model directly, for example openai/whisper-large-v2. See this program for more parameters.

python infer_tfs.py --audio_path=dataset/test.wav --model_path=models/whisper-tiny-finetune

Accelerate inference

As we all know, directly using the Whisper model reasoning is relatively slow, so here provides a way to accelerate, mainly using CTranslate2 for acceleration, first to transform the model, transform the combined model into CTranslate2 model. In the following command, the --model parameter is the path of the merged model, but it is also possible to use the original whisper model directly, such as openai/whisper-large-v2. The --output_dir parameter specifies the path of the transformed CTranslate2 model, and the --quantization parameter quantizes the model size. If you don't want to quantize the model, you can drop this parameter.

ct2-transformers-converter --model models/whisper-tiny-finetune --output_dir models/whisper-tiny-finetune-ct2 --copy_files tokenizer.json --quantization float16

Execute the following program to accelerate speech recognition, where the --audio_path argument specifies the audio path to predict. --model_path specifies the transformed CTranslate2 model. See this program for more parameters.

python infer_ct2.py --audio_path=dataset/test.wav --model_path=models/whisper-tiny-finetune-ct2

Output:

-----------  Configuration Arguments -----------
audio_path: dataset/test.wav
model_path: models/whisper-tiny-finetune-ct2
language: zh
use_gpu: True
use_int8: False
beam_size: 10
num_workers: 1
vad_filter: False
local_files_only: True
------------------------------------------------
[0.0 - 8.0]：近几年,不但我用书给女儿压碎,也全说亲朋不要给女儿压碎钱,而改送压碎书。

GUI inference

Here again, CTranslate2 is used for acceleration, and the transformation model is shown in the above documentation. --model_path specifies the transformed CTranslate2 model. See this program for more parameters.

python infer_gui.py --model_path=models/whisper-tiny-finetune-ct2

After startup, the screen is as follows:

Web deploy

Web deployment is also accelerated using CTranslate2, as shown in the documentation above. --host specifies the address where the service will be started, here 0.0.0.0, which means any address will be accessible. --port specifies the port number to use. --model_path specifies the transformed CTranslate2 model. --num_workers specifies how many threads to use for concurrent inference, which is important in Web deployments where multiple concurrent accesses can be inferred at the same time. See this program for more parameters.

python infer_server.py --host=0.0.0.0 --port=5000 --model_path=models/whisper-tiny-finetune-ct2 --num_workers=2

API docs

At present, two interfaces are provided, the common recognition interface /recognition and the stream return result /recognition_stream. Note that the stream refers to the stream return recognition result, which is also to upload the complete audio and then stream back the recognition result. This method is very good for long speech recognition experience. Their document interface is exactly the same, and the interface parameters are as follows.

Field	Need	type	Default	Explain
audio	Yes	File		Audio File
to_simple	No	int	1	Traditional Chinese to Simplified Chinese
remove_pun	No	int	0	Whether to remove punctuation
task	No	String	transcribe	Identify task types and support transcribe and translate
language	No	String	zh	Set the language, shorthand, to automatically detect the language if None

Return result:

Field	type	Explain
results	list	Recognition results separated into individual parts
+result	str	Text recognition result for each separated part
+start	int	Start time in seconds for each separated part
+end	int	End time in seconds for each separated part
code	int	Error code, 0 indicates successful recognition

Example:

{
  "results": [
    {
      "result": "近几年,不但我用书给女儿压碎,也全说亲朋不要给女儿压碎钱,而改送压碎书。",
      "start": 0,
      "end": 8
    }
  ],
  "code": 0
}

To make it easier to understand, here is the Python code to call the Web interface. Here is how to call /recognition.

import requests

response = requests.post(url="http://127.0.0.1:5000/recognition",
                         files=[("audio", ("test.wav", open("dataset/test.wav", 'rb'), 'audio/wav'))],
                         json={"to_simple": 1, "remove_pun": 0, "language": "zh", "task": "transcribe"}, timeout=20)
print(response.text)

Here is how /recognition stream is called.

import json
import requests

response = requests.post(url="http://127.0.0.1:5000/recognition_stream",
                         files=[("audio", ("test.wav", open("dataset/test_long.wav", 'rb'), 'audio/wav'))],
                         json={"to_simple": 1, "remove_pun": 0, "language": "zh", "task": "transcribe"}, stream=True,
                         timeout=20)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
    if chunk:
        result = json.loads(chunk.decode())
        text = result["result"]
        start = result["start"]
        end = result["end"]
        print(f"[{start} - {end}]：{text}")

The provided test page is as follows:

The home page http://127.0.0.1:5000/ looks like this:

Document page http://127.0.0.1:5000/docs page is as follows:

Android

The source code for the installation and deployment can be found in AndroidDemo and the documentation can be found in README.md.

Windows Desktop

The program is in the WhisperDesktop directory, and the documentation can be found in README.md.

Reference

https://github.com/huggingface/peft
https://github.com/guillaumekln/faster-whisper
https://github.com/ggerganov/whisper.cpp
https://github.com/Const-me/Whisper

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README_en.md

README_en.md

Fine-tune Whisper speech recognition models and speed up reasoning

Introduction

please ⭐

Supporting models

Catalogue

Introduction of the main program of the project

Model Description

Model CER(%) ↓

安装环境

Prepare the data

Fine-tune

Single-GPU

Multi-GPU

Merge model

Evaluation

Inference

Accelerate inference

GUI inference

Web deploy

API docs

Android

Windows Desktop

Reference

Files

README_en.md

Latest commit

History

README_en.md

File metadata and controls

Fine-tune Whisper speech recognition models and speed up reasoning

Introduction

please ⭐

Supporting models

Catalogue

Introduction of the main program of the project

Model Description

Model CER(%) ↓

安装环境

Prepare the data

Fine-tune

Single-GPU

Multi-GPU

Merge model

Evaluation

Inference

Accelerate inference

GUI inference

Web deploy

API docs

Android

Windows Desktop

Reference