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# 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-large-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)
```
🎉 |