Wav2vec2 Large Xlsr 53 Chinese Zh Cn Gpt

🚀 Wav2Vec2-Large-XLSR-53-Chinese-zh-cn-gpt

This model is fine-tuned from facebook/wav2vec2-large-xlsr-53 on Chinese (zh-CN), aiming to provide high - quality automatic speech recognition for Chinese.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-large-xlsr-53 on Chinese (zh - CN). It uses the Common Voice dataset, including the Common Voice Chinese (zh - TW) dataset (converting the label text to simplified Chinese). When using this model, ensure that your speech input is sampled at 16kHz.

✨ Features

• Fine - tuned on Chinese (zh - CN) language.
• Incorporates the Chinese (zh - TW) dataset with label text converted to simplified Chinese.
• Suitable for automatic speech recognition tasks in Chinese.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "zh-CN", split="test")

processor = Wav2Vec2Processor.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt")
model = Wav2Vec2ForCTC.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

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

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset[:2]["sentence"])

📚 Documentation

Evaluation

The model can be evaluated as follows on the zh - CN test data of Common Voice. The original CER calculation refers to https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese

#!pip install datasets==1.4.1
#!pip install transformers==4.4.0
#!pip install torchaudio
#!pip install jiwer

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import jiwer

def chunked_cer(targets, predictions, chunk_size=None):

    _predictions = [char for seq in predictions for char in list(seq)]
    _targets = [char for seq in targets for char in list(seq)]
    
    if chunk_size is None: return jiwer.wer(_targets, _predictions)
    
    start = 0
    end = chunk_size
    H, S, D, I = 0, 0, 0, 0
    
    while start < len(targets):
    
        _predictions = [char for seq in predictions[start:end] for char in list(seq)]
        _targets = [char for seq in targets[start:end] for char in list(seq)]
        chunk_metrics = jiwer.compute_measures(_targets, _predictions)
        H = H + chunk_metrics["hits"]
        S = S + chunk_metrics["substitutions"]
        D = D + chunk_metrics["deletions"]
        I = I + chunk_metrics["insertions"]
        start += chunk_size
        end += chunk_size
        
    return float(S + D + I) / float(H + S + D)
    
test_dataset = load_dataset("common_voice", "zh-CN", split="test")

processor = Wav2Vec2Processor.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt")
model = Wav2Vec2ForCTC.from_pretrained("ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt")
model.to("cuda")

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📄 License

This project is licensed under the Apache 2.0 license.

📊 Model Information