| # Wav2Vec2 Acoustic Model fine-tuned on LibriSpeech | |
| Original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. | |
| Paper: https://arxiv.org/abs/2006.11477 | |
| ## Usage | |
| 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: | |
| ```bash | |
| $ git checkout add_wav2vec | |
| ``` | |
| In the following, we'll show a simple example of how the model can be used for automatic speech recognition. | |
| First, let's load the model | |
| ```python | |
| from transformers import AutoModelForMaskedLM | |
| model = AutoModelForMaskedLM.from_pretrained("patrickvonplaten/wav2vec2-base-960h") | |
| ``` | |
| Next, let's load a dummy librispeech dataset | |
| ```python | |
| from datasets import load_dataset | |
| import soundfile as sf | |
| libri_speech_dummy = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") | |
| def map_to_array(batch): | |
| speech_array, _ = sf.read(batch["file"]) | |
| batch["speech"] = speech_array | |
| return batch | |
| libri_speech_dummy = libri_speech_dummy.map(map_to_array, remove_columns=["file"]) | |
| # check out dataset | |
| print(libri_speech_dummy) | |
| input_speech_16kHz = libri_speech_dummy[2]["speech"] | |
| expected_trans = libri_speech_dummy[2]["text"] | |
| ``` | |
| Cool, now we can run an inference pass to retrieve the logits: | |
| ```python | |
| import torch | |
| logits = model(torch.tensor(input_speech_16kHz)[None, :]) | |
| # use highest probability logits | |
| pred_ids = torch.argmax(logits[0], axis=-1) | |
| ``` | |
| Finally, let's decode the prediction. | |
| Let's create a simple CTC-Decoder: | |
| ```python | |
| import numpy as np | |
| from itertools import groupby | |
| class Decoder: | |
| def __init__(self, json_dict): | |
| self.dict = json_dict | |
| self.look_up = np.asarray(list(self.dict.keys())) | |
| def decode(self, ids): | |
| converted_tokens = self.look_up[ids] | |
| fused_tokens = [tok[0] for tok in groupby(converted_tokens)] | |
| output = ' '.join(''.join(''.join(fused_tokens).split("<s>")).split("|")) | |
| return output | |
| ``` | |
| and instantiate with the corresponding dict. | |
| ```python | |
| # hard-coded json dict taken from: https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt | |
| 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} | |
| decoder = Decoder(json_dict=json_dict) | |
| ``` | |
| and decode the result | |
| ```python | |
| pred_trans = decoder.decode(pred_ids) | |
| print("Prediction:\n", pred_trans) | |
| print("\n" + 50 * "=" + "\n") | |
| print("Correct result:\n", expected_trans) | |
| ``` | |
| 🎉 |