tmh 0.0.46

TMH Speech package

TMH Speech

TMH Speech is a library that gives access to open source models for transcription.

Read the docs

https://tmh-docs.readthedocs.io/en/latest/docs.html#getting-started

Getting started

To start the project you first need to install tmh and pyannote, since we are using newer packages.

pip install tmh
pip install https://github.com/pyannote/pyannote-audio/archive/develop.zip

Example usage

Transcription

from tmh.transcribe import transcribe_from_audio_path
file_path = "./sv.wav"
transcription = "Nu prövar vi att spela in ljud på svenska sex laxar i en laxask de finns en stor banan"
print("creating transcription")
asr_transcription = transcribe_from_audio_path(file_path)
print("output")
print(asr_transcription)
print("the transcription is", transcription)

Transcribe with VAD

from tmh.transcribe_with_vad import transcribe_from_audio_path_split_on_speech
file_path = "./sv.wav"
print("creating transcription")
asr_transcription_with_vad = transcribe_from_audio_path_split_on_speech(file_path)
print("transcription")
print(asr_transcription_with_vad)

Overlap detection

from tmh.overalp import overlap_detection

file_path = "./sv.wav"
overlap = overlap_detection(audio_path)
print(overlap)

Language classification

from tmh.transcribe import classify_language
file_path = "./sv.wav"
transcription = "Nu prövar vi att spela in ljud på svenska sex laxar i en laxask de finns en stor banan"
print("classifying language")
language = classify_language(file_path)
print("the language is", language)

Classify emotion

from tmh.transcribe import classify_emotion
file_path = "./sv.wav"
print("classifying emotion")
language = classify_emotion(file_path)
print("the emotion is", language)

Speaker embeddings

The speaker embeddings are made using the following library https://huggingface.co/speechbrain/spkrec-xvect-voxceleb

Extract speaker embedding

from tmh.transcribe import extract_speaker_embedding
file_path = "./sv.wav"
print("extracting speaker embedding")
embeddings = extract_speaker_embedding(file_path)
print("the speaker embedding is", embeddings)

Voice activity detection

from tmh.vad import extract_silences
file_path = "./sv.wav"
print("extracting silences")
embeddings = extract_silences(file_path)
print("the silences are", embeddings)

Speech Generation

Tacotron 2

Make sure you install these packages before running tacotron 2

pip install numpy scipy librosa unidecode inflect librosa
apt-get update
apt-get install -y libsndfile1

Text generation

You can use the text generation api to generate text based on any pretrained model from huggingface.

Example Swedish

from tmh.text import generate_text

output = generate_text(model='birgermoell/swedish-gpt', prompt="AI har möjligheten att", min_length=150)
print(output)

Example GPT-j

from tmh.text import generate_text

output = generate_text(model='EleutherAI/gpt-neo-2.7B', prompt="EleutherAI has", min_length=150)
print(output)

Codex

Generate code and save to file. To use

from tmh.code import generate_from_prompt, write_to_file
response = generate_from_prompt('''
A pytorch neural network model for MNIST
'''
)
write_to_file(response, "generated.py")

Build instructions

Change the version number

python3 -m build 
twine upload --skip-existing dist/*

Read the docs

https://tmh-docs.readthedocs.io/en/latest/docs.html#getting-started

Github

https://github.com/BirgerMoell/tmh