pytorch speech-encoder-decoder automatic-speech-recognition multilingual fr de es ca it ru zh pt fa et mn nl tr ar sv lv sl ta ja id cy en dataset:common_voice dataset:multilingual_librispeech dataset:covost2 arxiv:2111.09296 transformers speech xls_r xls_r_translation license:apache-2.0
---
language: 
- multilingual
- fr
- de
- es
- ca
- it
- ru
- zh
- pt
- fa
- et
- mn
- nl
- tr
- ar
- sv
- lv
- sl
- ta
- ja
- id
- cy
- en
datasets:
- common_voice
- multilingual_librispeech
- covost2
tags:
- speech
- xls_r
- automatic-speech-recognition
- xls_r_translation
pipeline_tag: automatic-speech-recognition
license: apache-2.0
widget:
- example_title: Swedish
  src: https://cdn-media.huggingface.co/speech_samples/cv_swedish_1.mp3
- example_title: Arabic
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_ar_19058308.mp3
- example_title: Russian
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3
- example_title: German
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_de_17284683.mp3
- example_title: French
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_fr_17299386.mp3
- example_title: Indonesian
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_id_19051309.mp3
- example_title: Italian
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_it_17415776.mp3
- example_title: Japanese
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_ja_19482488.mp3
- example_title: Mongolian
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_mn_18565396.mp3
- example_title: Dutch
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3
- example_title: Russian
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3
- example_title: Turkish
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_tr_17341280.mp3
- example_title: Catalan
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_ca_17367522.mp3
- example_title: English
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
- example_title: Dutch
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3
---

# Wav2Vec2-XLS-R-2b-21-EN

Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**

![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png)

This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. 
The encoder was warm-started from the [**`facebook/wav2vec2-xls-r-1b`**](https://huggingface.co/facebook/wav2vec2-xls-r-1b) checkpoint and
the decoder from the [**`facebook/mbart-large-50`**](https://huggingface.co/facebook/mbart-large-50) checkpoint.
Consequently, the encoder-decoder model was fine-tuned on 21 `{lang}` -> `en` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2).

The model can translate from the following spoken languages `{lang}` -> `en` (English):

{`fr`, `de`, `es`, `ca`, `it`, `ru`, `zh-CN`, `pt`, `fa`, `et`, `mn`, `nl`, `tr`, `ar`, `sv-SE`, `lv`, `sl`, `ta`, `ja`, `id`, `cy`} -> `en`

For more information, please refer to Section *5.1.2* of the [official XLS-R paper](https://arxiv.org/abs/2111.09296).

## Usage

### Demo

The model can be tested directly on the speech recognition widget on this model card! 
Simple record some audio in one of the possible spoken languages or pick an example audio file to see how well the checkpoint can translate the input.

### Example 

As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
transcripts by passing the speech features to the model.

You can use the model directly via the ASR pipeline

```python
from datasets import load_dataset
from transformers import pipeline

# replace following lines to load an audio file of your choice
librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
audio_file = librispeech_en[0]["file"]

asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-1b-21-to-en", feature_extractor="facebook/wav2vec2-xls-r-1b-21-to-en")

translation = asr(audio_file)
```

or step-by-step as follows:

```python
import torch
from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
from datasets import load_dataset

model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en")
processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en")

ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")

inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt")
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
transcription = processor.batch_decode(generated_ids)
```

## Results `{lang}` -> `en`

See the row of **XLS-R (1B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model.

![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/X-%3EEnglish.png)

## More XLS-R models for `{lang}` -> `en` Speech Translation

- [Wav2Vec2-XLS-R-300M-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-300m-21-to-en)
- [Wav2Vec2-XLS-R-1B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-1b-21-to-en)
- [Wav2Vec2-XLS-R-2B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-2b-21-to-en)
- [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)
