
# Fine-tuned XLS-R 1B model for speech recognition in English

Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on English using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [Multilingual LibriSpeech](https://www.openslr.org/94/), [TED-LIUMv3](https://www.openslr.org/51/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli).
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-english")
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 = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-english"
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-english --dataset mozilla-foundation/common_voice_8_0 --config en --split test
```

2. To evaluate on `speech-recognition-community-v2/dev_data`

```bash
python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-english --dataset speech-recognition-community-v2/dev_data --config en --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-english,
  title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {E}nglish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-english}},
  year={2022}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "en", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R Wav2Vec2 English by Jonatas Grosman", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "config": "en", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 21.05, "name": "Test WER"}, {"type": "cer", "value": 8.44, "name": "Test CER"}, {"type": "wer", "value": 17.31, "name": "Test WER (+LM)"}, {"type": "cer", "value": 7.77, "name": "Test CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "en"}, "metrics": [{"type": "wer", "value": 20.53, "name": "Dev WER"}, {"type": "cer", "value": 9.31, "name": "Dev CER"}, {"type": "wer", "value": 17.7, "name": "Dev WER (+LM)"}, {"type": "cer", "value": 8.93, "name": "Dev CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "en"}, "metrics": [{"type": "wer", "value": 17.88, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition en hf-asr-leaderboard mozilla-foundation/common_voice_8_0 robust-speech-event dataset:mozilla-foundation/common_voice_8_0 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


# Fine-tuned XLS-R 1B model for speech recognition in English

Fine-tuned facebook/wav2vec2-xls-r-1b on English using the train and validation splits of Common Voice 8.0, Multilingual LibriSpeech, TED-LIUMv3, 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 :)

## Usage

Using the HuggingSound library:



Writing your own inference script:



## Evaluation Commands

1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'



2. To evaluate on 'speech-recognition-community-v2/dev_data'



If you want to cite this model you can use this:


# Fine-tuned XLS-R 1B model for speech recognition in English

Fine-tuned facebook/wav2vec2-xls-r-1b on English using the train and validation splits of Common Voice 8.0, Multilingual LibriSpeech, TED-LIUMv3, 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 :) ## Usage

Using the HuggingSound library:



Writing your own inference script: ## Evaluation Commands

1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'



2. To evaluate on 'speech-recognition-community-v2/dev_data'



If you want to cite this model you can use this:
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # Fine-tuned XLS-R 1B model for speech recognition in English

Fine-tuned facebook/wav2vec2-xls-r-1b on English using the train and validation splits of Common Voice 8.0, Multilingual LibriSpeech, TED-LIUMv3, 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 :) ## Usage

Using the HuggingSound library:



Writing your own inference script: ## Evaluation Commands

1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'



2. To evaluate on 'speech-recognition-community-v2/dev_data'



If you want to cite this model you can use this:
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #en #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# Fine-tuned XLS-R 1B model for speech recognition in English

Fine-tuned facebook/wav2vec2-xls-r-1b on English using the train and validation splits of Common Voice 8.0, Multilingual LibriSpeech, TED-LIUMv3, 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 :)## Usage

Using the HuggingSound library:



Writing your own inference script:## Evaluation Commands

1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'



2. To evaluate on 'speech-recognition-community-v2/dev_data'



If you want to cite this model you can use this: