Wav2vec2 Large Xlsr 53 Dhivehi

🚀 瓦维2大XLSR 53迪维希语模型

本模型基于facebook/wav2vec2-large-xlsr-53，使用通用语音数据集在迪维希语上进行了微调。使用此模型时，请确保语音输入采样率为16kHz。

🚀 快速开始

本模型可直接使用（无需语言模型），具体使用方法如下。

💻 使用示例

基础用法

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

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

processor = Wav2Vec2Processor.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi")
model = Wav2Vec2ForCTC.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi")

# 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])

高级用法

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

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

processor = Wav2Vec2Processor.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi")
model = Wav2Vec2ForCTC.from_pretrained("shahukareem/wav2vec2-large-xlsr-53-dhivehi")
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"])))

测试结果：32.85%

📚 详细文档

评估

本模型可在通用语音迪维希语测试数据上进行评估，评估代码如下。

训练

训练使用了通用语音的train和validation数据集。

示例预测

reference: ކަރަންޓް ވައިރުކޮށް ބޮކި ހަރުކުރުން
predicted: ކަރަންޓް ވައިރުކޮށް ބޮކި ހަރުކުރުން
--
reference: ދެން އެކުދިންނާ ދިމާއަށް އަތް ދިށްކޮށްލެވެ
predicted: ދެން އެކުދިންނާ ދިމާއަށް އަތް ދިއްކޮށްލެވެ ް
--
reference: ރަކި ހިނިތުންވުމަކާއެކު އޭނާ އަމިއްލައަށް ތައާރަފްވި
predicted: ރަކި ހިނިތުންވުމަކާއެކު އޭނާ އަމިއްލައަށް ތައަރަފްވި
--
reference: ކޮޓަރީގެ ކުޑަދޮރުން ބޭރު ބަލަހައްޓައިގެން އިން ރޫނާގެ މޫނުމަތިން ފާޅުވަމުން ދިޔައީ ކަންބޮޑުވުމުގެ އަސަރުތައް
predicted: ކޮޓަރީގެ ކުޑަދޮރުން ބޭރު ބަލަހައްޓައިގެން އިން ރނާގެ މޫނުމަތިން ފާޅުވަމުން ދިޔައީ ކަންބޮޑުވުމުގެ އަސަރުތައް
--

📄 许可证

本模型使用的许可证为Apache-2.0。

📦 模型信息