语言: or 数据集:

• common_voice 指标:
• wer 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: odia XLSR Wav2Vec2 Large 2000 结果:
  • 任务: 名称: 语音识别 类型: 自动语音识别 数据集: 名称: Common Voice or 类型: common_voice 参数: or 指标:
    • 名称: 测试WER 类型: wer 值: 54.6

Wav2Vec2-Large-XLSR-53-or

基于facebook/wav2vec2-large-xlsr-53在odia语上使用Common Voice进行微调。
使用该模型时，请确保语音输入采样率为16kHz。

使用方法

该模型可以直接使用（无需语言模型）如下：

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

test_dataset = load_dataset("common_voice", "or", split="test[:2%]") 

processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 
model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-or") 

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

# 数据预处理
# 需要将音频文件读取为数组
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn 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():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

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

print("预测结果:", processor.batch_decode(predicted_ids))
print("参考文本:", test_dataset["sentence"][:2])

评估

该模型可以在Common Voice的odia测试数据上进行如下评估：

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

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

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

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'  
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# 数据预处理
# 需要将音频文件读取为数组
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# 评估函数
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

\tpred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn 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"])))

测试结果: 54.6%

训练

训练使用了Common Voice的train、validation和测试数据集，同时也用于预测和测试。

训练脚本可在[https://github.com/rahul-art/wav2vec2_or]找到