huggingsound 0.0.1

HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools.

HuggingSound

HuggingSound: A toolkit for speech-related tasks based on HuggingFace's tools.

I have no intention of building a very complex tool here. I just wanna have an easy-to-use toolkit for my speech-related experiments. I hope this library could be helpful for someone else too :)

Requirements

• Python 3.7+

Installation

$ pip install huggingsound

How to use it?

I'll try to summarize the usage of this toolkit. But many things will be missing from the documentation below. I promise to make it better soon. For now, you can open an issue if you have some questions or look at the source code to see how it works.

For speech-recognition

Inference

import torch
from huggingsound.recognition import Model, PyCTCLMDecoder

device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 1
model = Model("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)

audio_paths = ["/path/to/sagan.mp3", "/path/to/asimov.wav"]
transcriptions = model.transcribe(audio_paths, batch_size=batch_size)

# transcriptions format (a list of dicts, one for each audio file):
# [
#  {
#   "transcription": "extraordinary claims require extraordinary evidence", 
#   "start_timestamps": [100, 120, 140, 180, ...],
#   "end_timestamps": [120, 140, 180, 200, ...],
#   "probabilities": [0.95, 0.88, 0.9, 0.97, ...]
# },
# ...]
#
# as you can see, not only the transcription is returned but also the timestamps (in milliseconds) 
# and probabilities of each character of the transcription.

Inference (boosted by a language model)

import torch
from huggingsound.recognition import Model, PyCTCLMDecoder

device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 1
model = Model("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)

audio_paths = ["/path/to/sagan.mp3", "/path/to/asimov.wav"]

# We implemented 3 different decoders for that: PyCTCLMDecoder, ParlanceLMDecoder, and FlashlightLMDecoder
# Each decoder can have different performances and depends on different libraries (You'll need to install them manually first).
# We'll use the PyCTCLMDecoder (so "pip install pyctcdecode" first) in the following example, 
# but you can use any of the 3 decoders...

# The LM format used by the LM decoders is the KenLM format (arpa or binary file).
# You can download some LM files examples from here: 
# https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english/tree/main/language_model

lm_path = "path/to/your/lm_files/lm.binary"
unigrams_path = "path/to/your/lm_files/unigrams.txt"

lm_decoder = PyCTCLMDecoder(model.token_set, lm_path=lm_path, unigrams_path=unigrams_path)
transcriptions = model.transcribe(audio_paths, batch_size=batch_size, decoder=lm_decoder)

Evaluation

import torch
from huggingsound.recognition import Model, PyCTCLMDecoder

device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 1
model = Model("jonatasgrosman/wav2vec2-large-xlsr-53-english", device=device)

reference_transcriptions = [
    {"path": "/path/to/sagan.mp3", "transcription": "extraordinary claims require extraordinary evidence"},
    {"path": "/path/to/asimov.wav", "transcription": "violence is the last refuge of the incompetent"},
]

evaluation = model.evaluate(reference_transcriptions, inference_batch_size=batch_size)
# evaluation format: {"wer": 0.08, "cer": 0.02}

Fine-tuning

import torch
from huggingsound.trainer import TrainingArguments, ModelArguments
from huggingsound.recognition import Model, DefaultTextNormalizer, TokenSet

device = "cuda" if torch.cuda.is_available() else "cpu"
model = Model("facebook/wav2vec2-large-xlsr-53", device=device)
output_dir = "my/finetuned/model/output/dir"

# first of all, you need to define your model's token set
tokens = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
token_set = TokenSet(tokens)

# the lines below will load the training and model arguments objects with their default values, 
# you can change them if you want to, see the source code for the available arguments
training_args = TrainingArguments() 
model_args = ModelArguments() 

# define your train/eval data
train_data = [
    {"path": "/path/to/sagan.mp3", "transcription": "extraordinary claims require extraordinary evidence"},
    {"path": "/path/to/asimov.wav", "transcription": "violence is the last refuge of the incompetent"},
]
eval_data = [
    {"path": "/path/to/sagan2.mp3", "transcription": "absence of evidence is not evidence of absence"},
    {"path": "/path/to/asimov2.wav", "transcription": "the true delight is in the finding out rather than in the knowing"},
]

# and finally, fine-tune your model
model.finetune(
    output_dir, 
    train_data=train_data, 
    eval_data=eval_data,
    token_set=token_set, 
    training_args=training_args,
    model_args=model_args,
)

Troubleshooting

• If you are having trouble when loading MP3 files: $ sudo apt-get install ffmpeg

Want to help?

See the contribution guidelines if you'd like to contribute to HuggingSound project.

You don't even need to know how to code to contribute to the project. Even the improvement of our documentation is an outstanding contribution.

If this project has been useful for you, please share it with your friends. This project could be helpful for them too.

If you like this project and want to motivate the maintainers, give us a :star:. This kind of recognition will make us very happy with the work that we've done with :heart:

You can also

Citation

If you want to cite the tool you can use this:

@misc{grosman2022huggingsound,
  title={HuggingSound},
  author={Grosman, Jonatas},
  publisher={GitHub},
  journal={GitHub repository},
  howpublished={\url{https://github.com/jonatasgrosman/huggingsound}},
  year={2022}
}