readme: ---
  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-300M-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-300m`**](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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-300m-21-to-en", feature_extractor="facebook/wav2vec2-xls-r-300m-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-300m-21-to-en")
  processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-300m-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 (0.3B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model.
  
  ![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/X-%3EE