File size: 4,543 Bytes
b14e0b6 c8e3f8d b14e0b6 d589a60 b14e0b6 c99c005 b14e0b6 c99c005 6bfaa2c 309c461 6bfaa2c c99c005 b14e0b6 d9ea097 b14e0b6 be73230 1f76e35 b14e0b6 67c0211 1f76e35 67c0211 b14e0b6 98ec9ac b14e0b6 98ec9ac b14e0b6 65e7095 d9ea097 852f357 65e7095 8926743 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- pt
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: XLS-R Wav2Vec2 Portuguese by Jonatas Grosman
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pt
metrics:
- name: Test WER
type: wer
value: 8.7
- name: Test CER
type: cer
value: 2.55
- name: Test WER (+LM)
type: wer
value: 6.04
- name: Test CER (+LM)
type: cer
value: 1.98
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Dev WER
type: wer
value: 24.23
- name: Dev CER
type: cer
value: 11.3
- name: Dev WER (+LM)
type: wer
value: 19.41
- name: Dev CER (+LM)
type: cer
value: 10.19
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 18.8
---
# Fine-tuned XLS-R 1B model for speech recognition in Portuguese
Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co./facebook/wav2vec2-xls-r-1b) on Portuguese using the train and validation splits of [Common Voice 8.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_8_0), [CORAA](https://github.com/nilc-nlp/CORAA), [Multilingual TEDx](http://www.openslr.org/100), and [Multilingual LibriSpeech](https://www.openslr.org/94/).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
## Usage
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-portuguese")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "pt"
MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-portuguese"
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)
```
## Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-portuguese --dataset mozilla-foundation/common_voice_8_0 --config pt --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-portuguese --dataset speech-recognition-community-v2/dev_data --config pt --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{grosman2021xlsr-1b-portuguese,
title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {P}ortuguese},
author={Grosman, Jonatas},
howpublished={\url{https://huggingface.co./jonatasgrosman/wav2vec2-xls-r-1b-portuguese}},
year={2022}
}
``` |