Wav2vec2 Large Xlsr Czech

🚀 捷克語XLSR Wav2Vec2大模型53

本項目基於Common Voice數據集，對facebook/wav2vec2-large-xlsr-53模型進行了針對捷克語的微調。使用此模型時，請確保語音輸入的採樣率為16kHz。

✨ 主要特性

• 語言：捷克語（cs）
• 數據集：Common Voice
• 評估指標：詞錯誤率（WER）
• 標籤：音頻、自動語音識別、語音、XLSR微調周
• 許可證：Apache-2.0

模型信息

🚀 快速開始

模型使用

本模型可直接使用（無需語言模型），示例代碼如下：

💻 使用示例

基礎用法

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

test_dataset = load_dataset("common_voice", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")
model = Wav2Vec2ForCTC.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")

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["speech"][:2], 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["sentence"][:2])

模型評估

可在Common Voice的捷克語測試數據上對模型進行評估，示例代碼如下：

高級用法

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

test_dataset = load_dataset("common_voice", "cs", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")
model = Wav2Vec2ForCTC.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")
model.to("cuda")

chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", '«', '»', '—', '…', '(', ')', '*', '”', '“']
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
# Note: this models is trained ignoring accents on letters as below
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().strip()
    batch["sentence"] = re.sub(re.compile('[äá]'), 'a', batch['sentence'])
    batch["sentence"] = re.sub(re.compile('[öó]'), 'o', batch['sentence'])
    batch["sentence"] = re.sub(re.compile('[èé]'), 'e', batch['sentence'])
    batch["sentence"] = re.sub(re.compile("[ïí]"), 'i', batch['sentence'])
    batch["sentence"] = re.sub(re.compile("[üů]"), 'u', batch['sentence'])
    batch['sentence'] = re.sub('  ', ' ', batch['sentence'])
    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)

# Preprocessing the datasets.
# We need to read the aduio 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

測試結果：24.56

🔧 訓練信息

本模型使用了Common Voice數據集的train和validation子集進行訓練。訓練腳本即將在此處發佈。

📄 許可證

本項目採用Apache-2.0許可證。