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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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datasets: |
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- timit_asr |
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base_model: facebook/wav2vec2-base |
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--- |
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# Wav2Vec2-Base-TIMIT |
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Fine-tuned [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) |
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on the [timit_asr dataset](https://huggingface.co/datasets/timit_asr). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import soundfile as sf |
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import torch |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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model_name = "elgeish/wav2vec2-base-timit-asr" |
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processor = Wav2Vec2Processor.from_pretrained(model_name) |
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model = Wav2Vec2ForCTC.from_pretrained(model_name) |
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model.eval() |
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dataset = load_dataset("timit_asr", split="test").shuffle().select(range(10)) |
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char_translations = str.maketrans({"-": " ", ",": "", ".": "", "?": ""}) |
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def prepare_example(example): |
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example["speech"], _ = sf.read(example["file"]) |
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example["text"] = example["text"].translate(char_translations) |
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example["text"] = " ".join(example["text"].split()) # clean up whitespaces |
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example["text"] = example["text"].lower() |
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return example |
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dataset = dataset.map(prepare_example, remove_columns=["file"]) |
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inputs = processor(dataset["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") |
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with torch.no_grad(): |
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predicted_ids = torch.argmax(model(inputs.input_values).logits, dim=-1) |
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predicted_ids[predicted_ids == -100] = processor.tokenizer.pad_token_id # see fine-tuning script |
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predicted_transcripts = processor.tokenizer.batch_decode(predicted_ids) |
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for reference, predicted in zip(dataset["text"], predicted_transcripts): |
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print("reference:", reference) |
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print("predicted:", predicted) |
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print("--") |
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``` |
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Here's the output: |
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``` |
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reference: she had your dark suit in greasy wash water all year |
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predicted: she had your dark suit in greasy wash water all year |
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-- |
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reference: where were you while we were away |
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predicted: where were you while we were away |
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-- |
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reference: cory and trish played tag with beach balls for hours |
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predicted: tcory and trish played tag with beach balls for hours |
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-- |
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reference: tradition requires parental approval for under age marriage |
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predicted: tradition requires parrental proval for under age marrage |
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-- |
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reference: objects made of pewter are beautiful |
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predicted: objects made of puder are bautiful |
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-- |
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reference: don't ask me to carry an oily rag like that |
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predicted: don't o ask me to carry an oily rag like that |
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-- |
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reference: cory and trish played tag with beach balls for hours |
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predicted: cory and trish played tag with beach balls for ours |
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-- |
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reference: don't ask me to carry an oily rag like that |
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predicted: don't ask me to carry an oily rag like that |
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-- |
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reference: don't do charlie's dirty dishes |
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predicted: don't do chawly's tirty dishes |
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-- |
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reference: only those story tellers will remain who can imitate the style of the virtuous |
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predicted: only those story tillaers will remain who can imvitate the style the virtuous |
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``` |
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## Fine-Tuning Script |
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You can find the script used to produce this model |
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[here](https://github.com/elgeish/transformers/blob/cfc0bd01f2ac2ea3a5acc578ef2e204bf4304de7/examples/research_projects/wav2vec2/finetune_base_timit_asr.sh). |
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