patrickvonplaten
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README.md ADDED
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+ # Wav2Vec2 Acoustic Model fine-tuned on LibriSpeech
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+
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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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+
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+ Paper: https://arxiv.org/abs/2006.11477
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+
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+ ## Usage
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+
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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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+
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+ ```bash
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+ $ git checkout add_wav2vec
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+ ```
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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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+
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+ First, let's load the model
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+
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+ ```python
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+ from transformers import AutoModelForMaskedLM
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+
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+ model = AutoModelForMaskedLM.from_pretrained("patrickvonplaten/wav2vec2-base-960h")
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+
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+ ```
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+
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+ Next, let's load a dummy librispeech dataset
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+
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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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+
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+ libri_speech_dummy = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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+
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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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+
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+ libri_speech_dummy = libri_speech_dummy.map(map_to_array, remove_columns=["file"])
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+
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+ # check out dataset
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+ print(libri_speech_dummy)
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+
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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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+
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+ Cool, now we can run an inference pass to retrieve the logits:
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ and instantiate with the corresponding dict.
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+
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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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+
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+ decoder = Decoder(json_dict=json_dict)
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+ ```
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+
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+ and decode the result
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+
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+ ```python
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+ pred_trans = decoder.decode(pred_ids)
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+
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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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+ 🎉
config.json ADDED
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+ {
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+ "architectures": [
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+ "Wav2Vec2ForMaskedLM"
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+ ],
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+ "conv_bias": false,
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+ "conv_dim": [
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512
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+ ],
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+ "conv_kernel": [
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+ 10,
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+ 3,
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+ 3,
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+ 3,
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+ 3,
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+ 2,
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+ 2
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+ ],
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+ "conv_stride": [
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+ 5,
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+ 2,
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+ 2,
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+ 2,
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "do_stable_layer_norm": false,
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+ "feat_extract_activation": "gelu",
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+ "feat_extract_dropout": 0.0,
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+ "feat_extract_norm": "group",
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-05,
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 12,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "num_hidden_layers": 12,
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+ "transformers_version": "4.3.0.dev0",
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+ "vocab_size": 32
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+ }
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special_tokens_map.json ADDED
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+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
tokenizer_config.json ADDED
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+ {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": false}
vocab.json ADDED
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+ {"<pad>": 0, "<s>": 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}