|
--- |
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language: |
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- ab |
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- af |
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- ak |
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- am |
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- ar |
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- as |
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- av |
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- ay |
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- az |
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- ba |
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- bm |
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- be |
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- bn |
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- bi |
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- bo |
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- sh |
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- br |
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- bg |
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- ca |
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- cs |
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- ce |
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- cv |
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- ku |
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- cy |
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- da |
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- de |
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- dv |
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- dz |
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- el |
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- en |
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- eo |
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- et |
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- eu |
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- ee |
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- fo |
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- fa |
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- fj |
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- fi |
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- fr |
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- fy |
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- ff |
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- ga |
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- gl |
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- gn |
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- gu |
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- zh |
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- ht |
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- ha |
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- he |
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- hi |
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- sh |
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- hu |
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- hy |
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- ig |
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- ia |
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- ms |
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- is |
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- it |
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- jv |
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- ja |
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- kn |
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- ka |
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- kk |
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- kr |
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- km |
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- ki |
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- rw |
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- ky |
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- ko |
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- kv |
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- lo |
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- la |
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- lv |
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- ln |
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- lt |
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- lb |
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- lg |
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- mh |
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- ml |
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- mr |
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- ms |
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- mk |
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- mg |
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- mt |
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- mn |
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- mi |
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- my |
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- zh |
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- nl |
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- 'no' |
|
- 'no' |
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- ne |
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- ny |
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- oc |
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- om |
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- or |
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- os |
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- pa |
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- pl |
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- pt |
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- ms |
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- ps |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- qu |
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- ro |
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- rn |
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- ru |
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- sg |
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- sk |
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- sl |
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- sm |
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- sn |
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- sd |
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- so |
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- es |
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- sq |
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- su |
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- sv |
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- sw |
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- ta |
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- tt |
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- te |
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- tg |
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- tl |
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- th |
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- ti |
|
- ts |
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- tr |
|
- uk |
|
- ms |
|
- vi |
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- wo |
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- xh |
|
- ms |
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- yo |
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- ms |
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- zu |
|
- za |
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license: cc-by-nc-4.0 |
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tags: |
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- mms |
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- wav2vec2 |
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--- |
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|
|
# Forced Alignment with Hugging Face CTC Models |
|
This Python package provides an efficient way to perform forced alignment between text and audio using Hugging Face's pretrained models. it also features an improved implementation to use much less memory than TorchAudio forced alignment API. |
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|
|
The model checkpoint uploaded here is a conversion from torchaudio to HF Transformers for the MMS-300M checkpoint trained on forced alignment dataset |
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|
|
## Installation |
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|
|
```bash |
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pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git |
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``` |
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## Usage |
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|
|
```python |
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import torch |
|
from ctc_forced_aligner import ( |
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load_audio, |
|
load_alignment_model, |
|
generate_emissions, |
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preprocess_text, |
|
get_alignments, |
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get_spans, |
|
postprocess_results, |
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) |
|
|
|
audio_path = "your/audio/path" |
|
text_path = "your/text/path" |
|
language = "iso" # ISO-639-3 Language code |
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
batch_size = 16 |
|
|
|
|
|
alignment_model, alignment_tokenizer, alignment_dictionary = load_alignment_model( |
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device, |
|
dtype=torch.float16 if device == "cuda" else torch.float32, |
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) |
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|
|
audio_waveform = load_audio(audio_path, alignment_model.dtype, alignment_model.device) |
|
|
|
|
|
with open(text_path, "r") as f: |
|
lines = f.readlines() |
|
text = "".join(line for line in lines).replace("\n", " ").strip() |
|
|
|
emissions, stride = generate_emissions( |
|
alignment_model, audio_waveform, batch_size=batch_size |
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) |
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|
|
tokens_starred, text_starred = preprocess_text( |
|
text, |
|
romanize=True, |
|
language=language, |
|
) |
|
|
|
segments, scores, blank_id = get_alignments( |
|
emissions, |
|
tokens_starred, |
|
alignment_dictionary, |
|
) |
|
|
|
spans = get_spans(tokens_starred, segments, alignment_tokenizer.decode(blank_id)) |
|
|
|
word_timestamps = postprocess_results(text_starred, spans, stride, scores) |
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``` |
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|
