语言: 英语
数据集:

• 通用语音(common_voice)
  评估指标:
• 词错误率(wer)
• 字符错误率(cer)
  标签:
• 音频
• 自动语音识别
• 语音
• XLSR微调周
  许可证: Apache-2.0
  模型索引:
• 名称: Jonatas Grosman的Wav2Vec2英语模型
  结果:
  • 任务:
    名称: 语音识别
    类型: 自动语音识别
    数据集:
    名称: 通用语音英语数据集
    类型: common_voice
    参数: en
    指标:
    • 名称: 测试WER
      类型: wer
      值: 21.53
    • 名称: 测试CER
      类型: cer
      值: 9.66

Wav2vec2大型英语模型

基于facebook/wav2vec2-large在通用语音数据集上进行英语微调。
使用该模型时，请确保语音输入采样率为16kHz。

使用方法

该模型可直接使用（无需语言模型），如下所示...

使用ASRecognition库：

from asrecognition import ASREngine  

asr = ASREngine("en", model_path="jonatasgrosman/wav2vec2-large-english")  

audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]  
transcriptions = asr.transcribe(audio_paths)  

自定义推理脚本：

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

LANG_ID = "en"  
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"  
SAMPLES = 10  

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")  

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

# 数据预处理  
def speech_file_to_array_fn(batch):  
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)  
    batch["speech"] = speech_array  
    batch["sentence"] = batch["sentence"].upper()  
    return batch  

test_dataset = test_dataset.map(speech_file_to_array_fn)  
inputs = processor(test_dataset["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)  
predicted_sentences = processor.batch_decode(predicted_ids)  

for i, predicted_sentence in enumerate(predicted_sentences):  
    print("-" * 100)  
    print("参考文本:", test_dataset[i]["sentence"])  
    print("预测结果:", predicted_sentence)  

评估

可在通用语音英语测试集上按以下方式评估模型：

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

LANG_ID = "en"  
MODEL_ID = "jonatasgrosman/wav2vec2-large-english"  
DEVICE = "cuda"  

CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",  
                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",  
                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",  
                   "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",  
                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]  

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

wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py  
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py  

chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"  

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

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(chars_to_ignore_regex, "", batch["sentence"]).upper()  
    return batch  

test_dataset = test_dataset.map(speech_file_to_array_fn)  

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.upper() for x in result["pred_strings"]]  
references = [x.upper() 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, chunk_size=1000) * 100}")  

测试结果:

下表展示了模型的词错误率(WER)和字符错误率(CER)。我们也在其他模型上运行了相同的评估脚本。注意，由于不同评估脚本的特殊性，下表结果可能与已报告结果存在差异。