Wav2vec2 Large Chinese Zh Cn

🚀 Fine-tuned XLSR-53 large model for speech recognition in Chinese

This is a fine-tuned model based on facebook/wav2vec2-large-xlsr-53 for Chinese speech recognition, which can accurately transcribe Chinese speech.

🚀 Quick Start

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Chinese using the train and validation splits of Common Voice 6.1, CSS10 and ST-CMDS. When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned on RTX3090 for 50h.

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

✨ Features

• High Compatibility: Fine - tuned on multiple Chinese datasets, suitable for various Chinese speech scenarios.
• Ease of Use: Can be used directly without a language model.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

The model can be used directly (without a language model) as follows... Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("wbbbbb/wav2vec2-large-chinese-zh-cn")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)

📚 Documentation

Evaluation

The model can be evaluated as follows on the Chinese (zh-CN) test data of Common Voice.

import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import warnings
import os

os.environ["KMP_AFFINITY"] = ""


LANG_ID = "zh-CN"
MODEL_ID = "zh-CN-output-aishell"
DEVICE = "cuda"

test_dataset = load_dataset("common_voice", LANG_ID, split="test")

wer = load_metric("wer")
cer = load_metric("cer")



processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = (
        re.sub("([^\u4e00-\u9fa5\u0030-\u0039])", "", batch["sentence"]).lower() + " "
    )
    return batch


test_dataset = test_dataset.map(
    speech_file_to_array_fn,
    num_proc=15,
    remove_columns=['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],
)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(
        batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True
    )

    with torch.no_grad():
        logits = model(
            inputs.input_values.to(DEVICE),
            attention_mask=inputs.attention_mask.to(DEVICE),
        ).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch


result = test_dataset.map(evaluate, batched=True, batch_size=8)

predictions = [x.lower() for x in result["pred_strings"]]
references = [x.lower() for x in result["sentence"]]

print(
    f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}"
)
print(f"CER: {cer.compute(predictions=predictions, references=references) * 100}")

Test Result:

In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2022-07-18). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

📄 License

This model is licensed under the apache-2.0 license.

🔧 Technical Details

• Model Type: Fine - tuned XLSR - 53 large model
• Training Data: Train and validation splits of Common Voice 6.1, CSS10 and ST - CMDS
• Training Environment: Fine - tuned on RTX3090 for 50h

📖 Citation

If you want to cite this model you can use this:

@misc{grosman2021xlsr53-large-chinese,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {C}hinese},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/wbbbbb/wav2vec2-large-chinese-zh-cn}},
  year={2021}
}