pytorch speech-encoder-decoder automatic-speech-recognition multilingual en de tr fa sv mn zh cy ca sl et id ar ta lv ja dataset:common_voice dataset:multilingual_librispeech dataset:covost2 arxiv:2111.09296 transformers speech xls_r xls_r_translation license:apache-2.0
---
language:
- multilingual
- en 
- de
- tr
- fa
- sv
- mn
- zh
- cy
- ca
- sl
- et
- id
- ar
- ta
- lv
- ja
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: English
  src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3
---

# Wav2Vec2-XLS-R-2B-EN-15

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-2b`**](https://huggingface.co/facebook/wav2vec2-xls-r-2b) 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 15 `en` -> `{lang}` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2).

The model can translate from spoken `en` (Engish) to the following written languages `{lang}`:

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

For more information, please refer to Section *5.1.1* of the [official XLS-R paper](https://arxiv.org/abs/2111.09296).

## Usage

### Demo

The model can be tested on [**this space**](https://huggingface.co/spaces/facebook/XLS-R-2B-EN-15). 
You can select the target language, record some audio in English, 
and then sit back and 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. By default, the checkpoint will 
translate spoken English to written German. To change the written target language, 
you need to pass the correct `forced_bos_token_id` to `generate(...)` to condition 
the decoder on the correct target language. 

To select the correct `forced_bos_token_id` given your choosen language id, please make use 
of the following mapping:

```python
MAPPING = {
    "de": 250003,
    "tr": 250023,
    "fa": 250029,
    "sv": 250042,
    "mn": 250037,
    "zh": 250025,
    "cy": 250007,
    "ca": 250005,
    "sl": 250052,
    "et": 250006,
    "id": 250032,
    "ar": 250001,
    "ta": 250044,
    "lv": 250017,
    "ja": 250012,
}
```

As an example, if you would like to translate to Swedish, you can do the following:

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

# select correct `forced_bos_token_id`
forced_bos_token_id = MAPPING["sv"]

# 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-2b-en-to-15", feature_extractor="facebook/wav2vec2-xls-r-2b-en-to-15")

translation = asr(audio_file, forced_bos_token_id=forced_bos_token_id)
```

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-2b-en-to-15")
processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-2b-en-to-15")

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

# select correct `forced_bos_token_id`
forced_bos_token_id = MAPPING["sv"]

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"], forced_bos_token_id=forced_bos_token)
transcription = processor.batch_decode(generated_ids)
```

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

See the row of **XLS-R (2B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model.

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

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

- [Wav2Vec2-XLS-R-300M-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-300m-en-to-15)
- [Wav2Vec2-XLS-R-1B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-1b-en-to-15)
- [Wav2Vec2-XLS-R-2B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-2b-en-to-15)
- [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)
