đ Fine-tuned XLS-R 1B model for speech recognition in Italian
This is a fine-tuned model based on facebook/wav2vec2-xls-r-1b for Italian speech recognition, which can effectively convert Italian speech into text.
đ Quick Start
This model is fine-tuned on the facebook/wav2vec2-xls-r-1b using the train and validation splits of Common Voice 8.0, Multilingual TEDx, Multilingual LibriSpeech, and Voxpopuli.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool, and thanks to the GPU credits generously given by the OVHcloud :)
⨠Features
- Multilingual Adaptability: Based on the XLS - R Wav2Vec2 architecture, it shows good adaptability in Italian speech recognition.
- High - Quality Training: Trained on multiple high - quality datasets, including Common Voice 8.0, Multilingual TEDx, Multilingual LibriSpeech, and Voxpopuli.
- Performance Metrics: Achieved good results in WER (Word Error Rate) and CER (Character Error Rate) on different datasets.
đĻ Installation
There is no specific installation content provided in the original document. If you want to use this model, you can install the necessary libraries according to the usage examples, such as transformers
, torch
, librosa
, datasets
etc.
đģ Usage Examples
Basic Usage
Using the HuggingSound library:
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-italian")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
Advanced Usage
Writing your own inference script:
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "it"
MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-italian"
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)
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)
đ Documentation
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8_0
with split test
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset mozilla-foundation/common_voice_8_0 --config it --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-italian --dataset speech-recognition-community-v2/dev_data --config it --split validation --chunk_length_s 5.0 --stride_length_s 1.0
Model Index
Property |
Details |
Model Name |
XLS-R Wav2Vec2 Italian by Jonatas Grosman |
Task |
Automatic Speech Recognition |
Datasets |
- Common Voice 8 (mozilla - foundation/common_voice_8_0) - Robust Speech Event - Dev Data (speech - recognition - community - v2/dev_data) - Robust Speech Event - Test Data (speech - recognition - community - v2/eval_data) |
Metrics |
Common Voice 8 (Test): - Test WER: 9.04 - Test CER: 2.2 - Test WER (+LM): 6.75 - Test CER (+LM): 1.76 Robust Speech Event - Dev Data: - Dev WER: 23.38 - Dev CER: 9.41 - Dev WER (+LM): 15.84 - Dev CER (+LM): 8.93 Robust Speech Event - Test Data: - Test WER: 18.34 |
đ License
This model is licensed under the Apache - 2.0 license.
đ Citation
If you want to cite this model you can use this:
@misc{grosman2021xlsr-1b-italian,
title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {I}talian},
author={Grosman, Jonatas},
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-italian}},
year={2022}
}