speechlib 1.0.4

speechlib is a library that can do speaker diarization, transcription and speaker recognition on an audio file to create transcripts with actual speaker names. This library also contain audio preprocessor functions.

This library do speaker diarization, speaker recognition, transcription on a single wav file to provide a transcript with actual speaker names. This library will also return an array containing result information. This library also contain audio preprocessor functions.

Transcriptor takes 4 arguments. file to transcribe, log_folder, language used for transcribing, voices folder

voices_folder should contain subfolders named with speaker names and their voice samples. This will be used for speaker recognition to identify speaker.

if voice_folder is not provided then speaker tags will be arbitrary.

log_folder is to store final transcript as a text file.

Transcription example:

from speechlib import Transcriptor

file = "obama.wav"
voice_folder = "voices"
language = "english"
log_folder = "logs"

transcriptor = Transcriptor(file, log_folder, language, voice_folder)

res = transcriptor.transcribe()

print(res)

--> [["start", "end", "text", "speaker"], ["start", "end", "text", "speaker"]...]

start: starting time of speech
end: ending time of speech
text: transcribed text for speech during start and end
speaker: speaker of the text

supported languages:

['english', 'chinese', 'german', 'spanish', 'russian', 'korean', 'french', 'japanese', 'portuguese', 'turkish', 'polish', 'catalan', 'dutch', 'arabic', 'swedish', 'italian', 'indonesian', 'hindi', 'finnish', 'vietnamese', 'hebrew', 'ukrainian', 'greek', 'malay', 'czech', 'romanian', 'danish', 'hungarian', 'tamil', 'norwegian', 'thai', 'urdu', 'croatian', 'bulgarian', 'lithuanian', 'latin', 'maori', 'malayalam', 'welsh', 'slovak', 'telugu', 'persian', 'latvian', 'bengali', 'serbian', 'azerbaijani', 'slovenian', 'kannada', 'estonian', 'macedonian', 'breton', 'basque', 'icelandic', 'armenian', 'nepali', 'mongolian', 'bosnian', 'kazakh', 'albanian', 'swahili', 'galician', 'marathi', 'punjabi', 'sinhala', 'khmer', 'shona', 'yoruba', 'somali', 'afrikaans', 'occitan', 'georgian', 'belarusian', 'tajik', 'sindhi', 'gujarati', 'amharic', 'yiddish', 'lao', 'uzbek', 'faroese', 'haitian creole', 'pashto', 'turkmen', 'nynorsk', 'maltese', 'sanskrit', 'luxembourgish', 'myanmar', 'tibetan', 'tagalog', 'malagasy', 'assamese', 'tatar', 'hawaiian', 'lingala', 'hausa', 'bashkir', 'javanese', 'sundanese', 'burmese', 'valencian', 'flemish', 'haitian', 'letzeburgesch', 'pushto', 'panjabi', 'moldavian', 'moldovan', 'sinhalese', 'castilian']

Audio preprocessing example:

from speechlib import PreProcessor

file = "obama1.mp3"

# convert mp3 to wav
PreProcessor.mp3_to_wav(file)   

wav_file = "obama1.wav"

# convert wav file from stereo to mono
PreProcessor.convert_to_mono(wav_file)

# re-encode wav file to have 16-bit PCM encoding
PreProcessor.re_encode(wav_file)

This library uses following huggingface models:

https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb

https://huggingface.co/Ransaka/whisper-tiny-sinhala-20k-8k-steps-v2

https://huggingface.co/openai/whisper-medium

https://huggingface.co/pyannote/speaker-diarization