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  1. .gitignore +553 -0
  2. .vscode/settings.json +5 -0
  3. app.py +70 -0
  4. example1.wav +0 -0
  5. example2.mp3 +0 -0
  6. example3.mp3 +0 -0
  7. example4.wav +0 -0
  8. packages.txt +1 -0
  9. requirements.txt +7 -0
.gitignore ADDED
@@ -0,0 +1,553 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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.vscode/settings.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ {
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+ "files.watcherExclude": {
3
+ "**/target": true
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+ }
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+ }
app.py ADDED
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1
+ import gradio as gr
2
+ import torch
3
+ import zipfile
4
+ from pyctcdecode import build_ctcdecoder
5
+ from speechbrain.pretrained import EncoderASR
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+ from transformers.file_utils import cached_path, hf_bucket_url
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+
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+ cache_dir = './cache/'
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+ lm_file = hf_bucket_url("dragonSwing/wav2vec2-base-vn-270h", filename='4gram.zip')
10
+ lm_file = cached_path(lm_file, cache_dir=cache_dir)
11
+ with zipfile.ZipFile(lm_file, 'r') as zip_ref:
12
+ zip_ref.extractall(cache_dir)
13
+ lm_file = cache_dir + 'lm.binary'
14
+ vocab_file = cache_dir + 'vocab-260000.txt'
15
+ model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h",
16
+ savedir="/content/pretrained2/"
17
+ )
18
+
19
+ def get_decoder_ngram_model(tokenizer, ngram_lm_path, vocab_path=None):
20
+ unigrams = None
21
+ if vocab_path is not None:
22
+ unigrams = []
23
+ with open(vocab_path, encoding='utf-8') as f:
24
+ for line in f:
25
+ unigrams.append(line.strip())
26
+
27
+ vocab_dict = tokenizer.get_vocab()
28
+ sort_vocab = sorted((value, key) for (key, value) in vocab_dict.items())
29
+ vocab = [x[1] for x in sort_vocab]
30
+ vocab_list = vocab
31
+
32
+ # convert ctc blank character representation
33
+ vocab_list[tokenizer.pad_token_id] = ""
34
+ # replace special characters
35
+ vocab_list[tokenizer.word_delimiter_token_id] = " "
36
+ # specify ctc blank char index, since conventially it is the last entry of the logit matrix
37
+ decoder = build_ctcdecoder(vocab_list, ngram_lm_path, unigrams=unigrams)
38
+ return decoder
39
+
40
+ ngram_lm_model = get_decoder_ngram_model(model.tokenizer, lm_file, vocab_file)
41
+
42
+ def transcribe_file(path, max_seconds=20):
43
+ waveform = model.load_audio(path)
44
+ if max_seconds > 0:
45
+ waveform = waveform[:max_seconds*16000]
46
+ batch = waveform.unsqueeze(0)
47
+ rel_length = torch.tensor([1.0])
48
+ with torch.no_grad():
49
+ logits = model(batch, rel_length)
50
+ text_batch = [ngram_lm_model.decode(logit.detach().cpu().numpy(), beam_width=500) for logit in logits]
51
+ return text_batch[0]
52
+
53
+ def speech_recognize(file_upload, file_mic):
54
+ if file_upload is not None:
55
+ file = file_upload
56
+ elif file_mic is not None:
57
+ file = file_mic
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+ else:
59
+ return ""
60
+ # text = model.transcribe_file(file)
61
+ text = transcribe_file(file)
62
+ return text
63
+
64
+ inputs = [gr.inputs.Audio(source="upload", type='filepath', optional=True), gr.inputs.Audio(source="microphone", type='filepath', optional=True)]
65
+ outputs = gr.outputs.Textbox(label="Output Text")
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+ title = "wav2vec2-base-vietnamese-270h"
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+ description = "Gradio demo for a wav2vec2 base vietnamese speech recognition. To use it, simply upload your audio, click one of the examples to load them, or record from your own microphone. Read more at the links below. Currently supports 16_000hz audio files"
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+ article = "<p style='text-align: center'><a href='https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h' target='_blank'>Pretrained model</a></p>"
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+ examples=[['example1.wav', 'example1.wav'], ['example2.mp3', 'example2.mp3'], ['example3.mp3', 'example3.mp3'], ['example4.wav', 'example4.wav']]
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+ gr.Interface(speech_recognize, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()
example1.wav ADDED
Binary file (120 kB). View file
 
example2.mp3 ADDED
Binary file (411 kB). View file
 
example3.mp3 ADDED
Binary file (218 kB). View file
 
example4.wav ADDED
Binary file (76.8 kB). View file
 
packages.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ libsndfile1
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ torch==1.9.0
2
+ torchaudio==0.9.1
3
+ transformers==4.12.5
4
+ datasets==1.16.4
5
+ pyctcdecode==0.1.1
6
+ https://github.com/kpu/kenlm/archive/master.zip
7
+ git+git://github.com/speechbrain/speechbrain.git@develop