96 lines
2.7 KiB
Markdown
96 lines
2.7 KiB
Markdown
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# Wav2Vec2 Acoustic Model fine-tuned on LibriSpeech
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Original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
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Paper: https://arxiv.org/abs/2006.11477
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## Usage
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Make sure you are working on [this branch](https://github.com/huggingface/transformers/tree/add_wav2vec) (which will be merged to master soon hopefully) of transformers:
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```bash
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$ git checkout add_wav2vec
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```
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In the following, we'll show a simple example of how the model can be used for automatic speech recognition.
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First, let's load the model
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```python
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from transformers import AutoModelForMaskedLM
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model = AutoModelForMaskedLM.from_pretrained("patrickvonplaten/wav2vec2-base-960h")
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```
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Next, let's load a dummy librispeech dataset
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```python
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from datasets import load_dataset
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import soundfile as sf
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libri_speech_dummy = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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def map_to_array(batch):
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speech_array, _ = sf.read(batch["file"])
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batch["speech"] = speech_array
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return batch
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libri_speech_dummy = libri_speech_dummy.map(map_to_array, remove_columns=["file"])
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# check out dataset
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print(libri_speech_dummy)
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input_speech_16kHz = libri_speech_dummy[2]["speech"]
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expected_trans = libri_speech_dummy[2]["text"]
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```
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Cool, now we can run an inference pass to retrieve the logits:
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```python
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import torch
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logits = model(torch.tensor(input_speech_16kHz)[None, :])
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# use highest probability logits
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pred_ids = torch.argmax(logits[0], axis=-1)
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```
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Finally, let's decode the prediction.
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Let's create a simple CTC-Decoder:
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```python
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import numpy as np
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from itertools import groupby
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class Decoder:
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def __init__(self, json_dict):
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self.dict = json_dict
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self.look_up = np.asarray(list(self.dict.keys()))
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def decode(self, ids):
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converted_tokens = self.look_up[ids]
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fused_tokens = [tok[0] for tok in groupby(converted_tokens)]
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output = ' '.join(''.join(''.join(fused_tokens).split("<s>")).split("|"))
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return output
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```
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and instantiate with the corresponding dict.
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```python
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# hard-coded json dict taken from: https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
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json_dict = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3, "|": 4, "E": 5, "T": 6, "A": 7, "O": 8, "N": 9, "I": 10, "H": 11, "S": 12, "R": 13, "D": 14, "L": 15, "U": 16, "M": 17, "W": 18, "C": 19, "F": 20, "G": 21, "Y": 22, "P": 23, "B": 24, "V": 25, "K": 26, "'": 27, "X": 28, "J": 29, "Q": 30, "Z": 31}
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decoder = Decoder(json_dict=json_dict)
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```
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and decode the result
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```python
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pred_trans = decoder.decode(pred_ids)
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print("Prediction:\n", pred_trans)
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print("\n" + 50 * "=" + "\n")
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print("Correct result:\n", expected_trans)
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```
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🎉
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