模型:
jonatasgrosman/wav2vec2-xls-r-1b-spanish
使用 Common Voice 8.0 , MediaSpeech , Multilingual TEDx , Multilingual LibriSpeech 和 Voxpopuli 的训练和验证数据集对 facebook/wav2vec2-xls-r-1b 进行了西班牙语调优。使用此模型时,请确保语音输入的采样率为16kHz。
此模型是通过 HuggingSound 工具进行调优的,并且感谢 OVHcloud 慷慨赠予的GPU积分:)
使用 HuggingSound 库:
from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-spanish") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths)
编写您自己的推理脚本:
import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "es" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-spanish" 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) # Preprocessing the datasets. # We need to read the audio files as arrays 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)
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-spanish --dataset mozilla-foundation/common_voice_8_0 --config es --split test
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-spanish --dataset speech-recognition-community-v2/dev_data --config es --split validation --chunk_length_s 5.0 --stride_length_s 1.0
如果您想引用此模型,可以使用以下内容:
@misc{grosman2021xlsr-1b-spanish, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {S}panish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish}}, year={2022} }