mms-300m-1130-forced-aligner (ONNX)
This is an ONNX version of MahmoudAshraf/mms-300m-1130-forced-aligner. It was automatically converted and uploaded using this Hugging Face Space.
Usage with Transformers.js
See the pipeline documentation for automatic-speech-recognition: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline
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.
The model checkpoint uploaded here is a conversion from torchaudio to HF Transformers for the MMS-300M checkpoint trained on forced alignment dataset
Installation
pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
Usage
import torch
from ctc_forced_aligner import (
load_audio,
load_alignment_model,
generate_emissions,
preprocess_text,
get_alignments,
get_spans,
postprocess_results,
)
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 = load_alignment_model(
device,
dtype=torch.float16 if device == "cuda" else torch.float32,
)
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
)
tokens_starred, text_starred = preprocess_text(
text,
romanize=True,
language=language,
)
segments, scores, blank_token = get_alignments(
emissions,
tokens_starred,
alignment_tokenizer,
)
spans = get_spans(tokens_starred, segments, blank_token)
word_timestamps = postprocess_results(text_starred, spans, stride, scores)