Wav2vec2 Large Xlsr Or

🚀 Wav2Vec2-Large-XLSR-53-or

本項目是在奧里亞語（odia）上對 facebook/wav2vec2-large-xlsr-53 進行微調得到的模型。使用該模型時，請確保輸入的語音採樣率為 16kHz。

模型信息

🚀 快速開始

模型使用

本模型可以直接使用（無需語言模型），示例代碼如下：

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)

# 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

可以在 Common Voice 的奧里亞語測試數據上對模型進行評估，示例代碼如下：

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)

# 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：54.6 %

模型訓練

使用了 Common Voice 的訓練集、驗證集和測試集進行訓練、預測和測試。訓練腳本可在 [https://github.com/rahul-art/wav2vec2_or] 找到。