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--- |
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language: en |
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datasets: |
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- timit_asr |
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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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license: apache-2.0 |
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--- |
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# Wav2Vec2-Large-LV60-TIMIT |
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Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) |
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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 = "hktayal345/wav2vec2-large-lv60-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: the emblem depicts the acropolis all aglow |
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predicted: the amblum depicts the acropolis all a glo |
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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: they enjoy it when i audition |
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predicted: they enjoy it when i addition |
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-- |
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reference: set aside to dry with lid on sugar bowl |
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predicted: set aside to dry with a litt on shoogerbowl |
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-- |
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reference: a boring novel is a superb sleeping pill |
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predicted: a bor and novel is a suberb sleeping peel |
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-- |
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reference: only the most accomplished artists obtain popularity |
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predicted: only the most accomplished artists obtain popularity |
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-- |
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reference: he has never himself done anything for which to be hated which of us has |
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predicted: he has never himself done anything for which to be hated which of us has |
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-- |
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reference: the fish began to leap frantically on the surface of the small lake |
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predicted: the fish began to leap frantically on the surface of the small lake |
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-- |
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reference: or certain words or rituals that child and adult go through may do the trick |
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predicted: or certain words or rituals that child an adult go through may do the trick |
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-- |
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reference: are your grades higher or lower than nancy's |
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predicted: are your grades higher or lower than nancies |
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-- |
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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://colab.research.google.com/drive/1gVaZhFuIXxBDN2pD0esW490azlbQtQ7C?usp=sharing). |
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**Note:** This model can be fine-tuned further; |
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[trainer_state.json](https://huggingface.co/harshit345/wav2vec2-large-lv60-timit/blob/main/trainer_state.json) |
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shows useful details, namely the last state (this checkpoint): |
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```json |
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{ |
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"epoch": 29.51, |
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"eval_loss": 25.424150466918945, |
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"eval_runtime": 182.9499, |
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"eval_samples_per_second": 9.183, |
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"eval_wer": 0.1351704233095107, |
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"step": 8500 |
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} |
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``` |
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