wav2vec2-xls-r-1b-portuguese / README.md

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	---
	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}
	}
	```