Metadata-Version: 2.1
Name: tmh
Version: 0.0.17
Summary: TMH Speech package
Home-page: https://pypi.org/project/tmh/
Author: Birger Moell
Author-email: <bmoell@kth.se>
License: UNKNOWN
Project-URL: Bug Tracker, https://pypi.org/project/tmh/
Keywords: python,speech,voice
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
License-File: LICENSE


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

## Example usage

### Transcription
``` python
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)
```

### Language classification
``` python
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
``` python
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
## https://huggingface.co/speechbrain/spkrec-xvect-voxceleb

### Extract speaker embedding
``` python
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
``` python
from tmh.vad import extract_silences
file_path = "./sv.wav"
print("extracting silences")
embeddings = extract_silences(file_path)
print("the silences are", embeddings)
```



