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1from huggingsound import SpeechRecognitionModel
2
3model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-french")
4audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
5
6transcriptions = model.transcribe(audio_paths)1import torch
2import librosa
3from datasets import load_dataset
4from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
5
6LANG_ID = "fr"
7MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-french"
8SAMPLES = 10
9
10test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
11
12processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
13model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
14
15# Preprocessing the datasets.
16# We need to read the audio files as arrays
17def speech_file_to_array_fn(batch):
18 speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
19 batch["speech"] = speech_array
20 batch["sentence"] = batch["sentence"].upper()
21 return batch
22
23test_dataset = test_dataset.map(speech_file_to_array_fn)
24inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
25
26with torch.no_grad():
27 logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
28
29predicted_ids = torch.argmax(logits, dim=-1)
30predicted_sentences = processor.batch_decode(predicted_ids)mozilla-foundation/common_voice_8_0 with split testpython eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset mozilla-foundation/common_voice_8_0 --config fr --split testspeech-recognition-community-v2/dev_datapython eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.01@misc{grosman2021xlsr-1b-french,
2 title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {F}rench},
3 author={Grosman, Jonatas},
4 howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-french}},
5 year={2022}
6}