🚀 希伯來語XLSR Wav2Vec2大模型53

本項目是在多個從YouTube下載的樣本上對facebook/wav2vec2-large-xlsr-53進行微調得到的模型。該模型可用於希伯來語的自動語音識別任務，使用時請確保輸入語音的採樣率為16kHz。

🚀 快速開始

本模型可直接使用（無需語言模型），以下是使用步驟和示例代碼。

💻 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", "he", split="test[:2%]") # there is no common dataset for Hebrew, please, paste your data
processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew")
model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew")
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():
  tlogits = 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])

高級用法

模型可以在一些希伯來語測試數據上進行評估，以下是評估代碼：

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "he", split="test") # there is no common dataset for Hebrew, please, paste your data
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew")
model = Wav2Vec2ForCTC.from_pretrained("imvladikon/wav2vec2-large-xlsr-53-hebrew").to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
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):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  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：

示例預測

📄 許可證

本項目採用Apache-2.0許可證。

📦 模型信息