Create README.md

README.md

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+---
+language:
+- en
+- ar
+datasets:
+- covost2
+- librispeech_asr
+tags:
+- audio
+- speech-translation
+- automatic-speech-recognition
+- speech2text2
+license: MIT
+pipeline_tag: automatic-speech-recognition
+widget:
+- label: Librispeech sample 1
+  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+- label: Librispeech sample 2
+  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+---
+
+
+# S2T2-Wav2Vec2-CoVoST2-EN-AR-ST
+
+`s2t-wav2vec2-large-en-ar` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
+The S2T model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in
+[Fairseq](https://github.com/pytorch/fairseq/blob/6f847c8654d56b4d1b1fbacec027f47419426ddb/fairseq/models/wav2vec/wav2vec2_asr.py#L266).
+
+
+## Model description
+
+S2T2 is a transformer-based seq2seq (speech encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
+Translation (ST). It uses a pretrained [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html) as the encoder and a transformer-based decoder. The model is trained with standard autoregressive cross-entropy loss and generates the translations autoregressively.
+
+## Intended uses & limitations
+
+This model can be used for end-to-end English speech to German text translation.
+See the [model hub](https://huggingface.co/models?filter=speech2text2) to look for other S2T2 checkpoints.
+
+
+### How to use
+
+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
+
+librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-ar", feature_extractor="facebook/s2t-wav2vec2-large-en-ar")
+
+translation_de = asr(librispeech_en[0]["file"])
+```
+
+or step-by-step as follows:
+
+```python
+import torch
+from transformers import Speech2Text2Processor, SpeechEncoderDecoder
+from datasets import load_dataset
+
+import soundfile as sf
+model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-ar")
+processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-ar")
+
+def map_to_array(batch):
+    speech, _ = sf.read(batch["file"])
+    batch["speech"] = speech
+    return batch
+    
+ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.map(map_to_array)
+
+inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
+generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
+transcription = processor.batch_decode(generated_ids)
+```
+
+## Evaluation results
+
+CoVoST-V2 test results for en-ar (BLEU score): **20.8**
+
+For more information, please have a look at the [official paper](https://arxiv.org/pdf/2104.06678.pdf)
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2104-06678,
+  author    = {Changhan Wang and
+               Anne Wu and
+               Juan Miguel Pino and
+               Alexei Baevski and
+               Michael Auli and
+               Alexis Conneau},
+  title     = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation},
+  journal   = {CoRR},
+  volume    = {abs/2104.06678},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2104.06678},
+  archivePrefix = {arXiv},
+  eprint    = {2104.06678},
+  timestamp = {Thu, 12 Aug 2021 15:37:06 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-06678.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+```