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--- |
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language: fa |
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datasets: |
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- common_voice_6_1 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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license: apache-2.0 |
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model-index: |
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- name: Sharif-wav2vec2 |
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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: Common Voice Corpus 6.1 (clean) |
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type: common_voice_6_1 |
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config: clean |
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split: test |
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args: |
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language: fa |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 6.0 |
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--- |
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# Sharif-wav2vec2 |
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[Sharif-wav2vec2](https://huggingface.co/SLPL/Sharif-wav2vec2/) |
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The base model fine-tuned on 108 hours of Commonvoice on 16kHz sampled speech audio. When using the model |
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make sure that your speech input is also sampled at 16Khz. |
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#[Paper](https://arxiv.org/abs/2006.11477) |
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#Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli |
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#**Abstract** |
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#We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can #outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and #solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all #labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec #2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of #labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech #recognition with limited amounts of labeled data. |
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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 Persian 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, Wav2Vec2ForCTC |
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from datasets import load_dataset |
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import torch |
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# load model and tokenizer |
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processor = Wav2Vec2Processor.from_pretrained("SLPL/Sharif-wav2vec2") |
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model = Wav2Vec2ForCTC.from_pretrained("SLPL/Sharif-wav2vec2") |
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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 # Batch size 1 |
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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-base-960h** 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 Wav2Vec2ForCTC, 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 = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") |
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processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") |
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def map_to_pred(batch): |
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input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values |
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with torch.no_grad(): |
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logits = model(input_values.to("cuda")).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, batched=True, batch_size=1, 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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| 3.4 | 8.6 | |
