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--- |
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language: en |
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datasets: |
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- librispeech_asr |
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tags: |
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- speech |
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- audio |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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license: apache-2.0 |
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model-index: |
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- name: wav2vec2-conformer-rel-pos-large-960h-ft |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Librispeech (clean) |
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type: librispeech_asr |
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args: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 1.96 |
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--- |
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# Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings |
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[Facebook's Wav2Vec2 Conformer (TODO-add link)]() |
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Wav2Vec2 Conformer with rotary position embeddings, pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. |
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[Paper (TODO)](https://arxiv.org/abs/2006.11477) |
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Authors: ... |
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**Abstract** |
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... |
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The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. |
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# Usage |
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To transcribe audio files the model can be used as a standalone acoustic model as follows: |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC |
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from datasets import load_dataset |
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import torch |
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# load model and processor |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") |
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model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") |
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# load dummy dataset and read soundfiles |
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ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") |
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# tokenize |
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input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values |
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# retrieve logits |
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logits = model(input_values).logits |
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# take argmax and decode |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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``` |
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## Evaluation |
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This code snippet shows how to evaluate **facebook/wav2vec2-conformer-rope-large-960h-ft** on LibriSpeech's "clean" and "other" test data. |
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```python |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ConformerForCTC, Wav2Vec2Processor |
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import torch |
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from jiwer import wer |
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librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") |
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model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft").to("cuda") |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft") |
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def map_to_pred(batch): |
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inputs = processor(batch["audio"]["array"], return_tensors="pt", padding="longest") |
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input_values = inputs.input_values.to("cuda") |
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attention_mask = inputs.attention_mask.to("cuda") |
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with torch.no_grad(): |
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logits = model(input_values, attention_mask=attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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batch["transcription"] = transcription |
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return batch |
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result = librispeech_eval.map(map_to_pred, remove_columns=["audio"]) |
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print("WER:", wer(result["text"], result["transcription"])) |
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``` |
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*Result (WER)*: |
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| "clean" | "other" | |
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|---|---| |
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| 1.96 | 3.98 | |
