🤗 Speechbox offers a set of speech processing tools, such as punctuation restoration.
With pip (official package)
pip install speechboxWe ❤️ contributions from the open-source community! If you want to contribute to this library, please check out our Contribution guide. You can look out for issues you'd like to tackle to contribute to the library.
- See Good first issues for general opportunities to contribute
- See New Task for more advanced contributions. Make sure to have read the Philosophy guide to succesfully add a new task.
Also, say 👋 in our public Discord channel 
| Task | Description | Author |
|---|---|---|
| Punctuation Restoration | Punctuation restoration allows one to predict capitalized words as well as punctuation by using Whisper. | Patrick von Platen |
| ASR With Speaker Diarization | Transcribe long audio files, such as meeting recordings, with speaker information (who spoke when) and the transcribed text. | Sanchit Gandhi |
Punctuation restoration relies on the premise that Whisper can understand universal speech. The model is forced to predict the passed words, but is allowed to capitalized letters, remove or add blank spaces as well as add punctuation. Punctuation is simply defined as the offial Python string.Punctuation characters.
Note: For now this package has only been tested with:
and only on some 80 audio samples of patrickvonplaten/librispeech_asr_dummy.
See some transcribed results here.
If you want to try out the punctuation restoration, you can try out the following 🚀 Spaces:
In order to use the punctuation restoration task, you need to install Transformers:
pip install --upgrade transformers
For this example, we will additionally make use of datasets to load a sample audio file:
pip install --upgrade datasets
Now we stream a single audio sample, load the punctuation restoring class with "openai/whisper-tiny.en" and add punctuation to the transcription.
from speechbox import PunctuationRestorer
from datasets import load_dataset
streamed_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# get first sample
sample = next(iter(streamed_dataset))
# print out normalized transcript
print(sample["text"])
# => "HE WAS IN A FEVERED STATE OF MIND OWING TO THE BLIGHT HIS WIFE'S ACTION THREATENED TO CAST UPON HIS ENTIRE FUTURE"
# load the restoring class
restorer = PunctuationRestorer.from_pretrained("openai/whisper-tiny.en")
restorer.to("cuda")
restored_text, log_probs = restorer(sample["audio"]["array"], sample["text"], sampling_rate=sample["audio"]["sampling_rate"], num_beams=1)
print("Restored text:\n", restored_text)See examples/restore for more information.
Given an unlabelled audio segment, a speaker diarization model is used to predict "who spoke when". These speaker predictions are paired with the output of a speech recognition system (e.g. Whisper) to give speaker-labelled transcriptions.
The combined ASR + Diarization pipeline can be applied directly to long audio samples, such as meeting recordings, to give fully annotated meeting transcriptions.
If you want to try out the ASR + Diarization pipeline, you can try out the following Space:
In order to use the ASR + Diarization pipeline, you need to install 🤗 Transformers and pyannote.audio:
pip install --upgrade transformers pyannote.audio
For this example, we will additionally make use of 🤗 Datasets to load a sample audio file:
pip install --upgrade datasets
Now we stream a single audio sample, pass it to the ASR + Diarization pipeline, and return the speaker-segmented transcription:
import torch
from speechbox import ASRDiarizationPipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipeline = ASRDiarizationPipeline.from_pretrained("openai/whisper-tiny", device=device)
# load dataset of concatenated LibriSpeech samples
concatenated_librispeech = load_dataset("sanchit-gandhi/concatenated_librispeech", split="train", streaming=True)
# get first sample
sample = next(iter(concatenated_librispeech))
out = pipeline(sample["audio"])
print(out)