207 lines
8.2 KiB
Markdown
207 lines
8.2 KiB
Markdown
<p align="center">
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<img src="speechlib.png" />
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</p>
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<p align="center">
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<a href="./LICENSE"><img src="https://img.shields.io/github/license/Navodplayer1/speechlib"></a>
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<a href="https://github.com/Navodplayer1/speechlib/releases"><img src="https://img.shields.io/github/v/release/Navodplayer1/speechlib?color=ffa"></a>
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<a href="support os"><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/python-3.8+-aff.svg"></a>
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<a href="https://github.com/Navodplayer1/speechlib/issues"><img src="https://img.shields.io/github/issues/Navodplayer1/speechlib?color=9cc"></a>
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<a href="https://github.com/Navodplayer1/speechlib/stargazers"><img src="https://img.shields.io/github/stars/Navodplayer1/speechlib?color=ccf"></a>
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<a href="https://pypi.org/project/speechlib/"><img src="https://static.pepy.tech/badge/speechlib"></a>
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</p>
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### Install torch torchaudio with CUDA support:
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pip3 install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
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### Install torch torchaudio without CUDA support:
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pip3 install torch torchaudio
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### Run your IDE as administrator
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you will get following error if administrator permission is not there:
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**OSError: [WinError 1314] A required privilege is not held by the client**
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### Requirements
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* Python 3.8 or greater
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### GPU execution
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GPU execution needs CUDA 11.
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GPU execution requires the following NVIDIA libraries to be installed:
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* [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas)
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* [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn)
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There are multiple ways to install these libraries. The recommended way is described in the official NVIDIA documentation, but we also suggest other installation methods below.
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### Google Colab:
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on google colab run this to install CUDA dependencies:
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```
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!apt install libcublas11
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```
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You can see this example [notebook](https://colab.research.google.com/drive/1lpoWrHl5443LSnTG3vJQfTcg9oFiCQSz?usp=sharing)
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### installation:
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```
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pip install speechlib
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```
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This library does speaker diarization, speaker recognition, and transcription on a single wav file to provide a transcript with actual speaker names. This library will also return an array containing result information. ⚙
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This library contains following audio preprocessing functions:
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1. convert other audio formats to wav
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2. convert stereo wav file to mono
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3. re-encode the wav file to have 16-bit PCM encoding
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Transcriptor method takes 7 arguments.
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1. file to transcribe
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2. log_folder to store transcription
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3. language used for transcribing (language code is used)
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4. model size ("tiny", "small", "medium", "large", "large-v1", "large-v2", "large-v3")
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5. ACCESS_TOKEN: huggingface acccess token
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1. Permission to access `pyannote/speaker-diarization@2.1` and `pyannote/segmentation`
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2. Token requires permission for 'Read access to contents of all public gated repos you can access'
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6. voices_folder (contains speaker voice samples for speaker recognition)
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7. quantization: this determine whether to use int8 quantization or not. Quantization may speed up the process but lower the accuracy.
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voices_folder should contain subfolders named with speaker names. Each subfolder belongs to a speaker and it can contain many voice samples. This will be used for speaker recognition to identify the speaker.
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if voices_folder is not provided then speaker tags will be arbitrary.
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log_folder is to store the final transcript as a text file.
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transcript will also indicate the timeframe in seconds where each speaker speaks.
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### Transcription example:
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```
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import os
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from speechlib import Transcriptor
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file = "obama_zach.wav" # your audio file
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voices_folder = "" # voices folder containing voice samples for recognition
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language = "en" # language code
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log_folder = "logs" # log folder for storing transcripts
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modelSize = "tiny" # size of model to be used [tiny, small, medium, large-v1, large-v2, large-v3]
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quantization = False # setting this 'True' may speed up the process but lower the accuracy
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ACCESS_TOKEN = "huggingface api key" # get permission to access pyannote/speaker-diarization@2.1 on huggingface
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# quantization only works on faster-whisper
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transcriptor = Transcriptor(file, log_folder, language, modelSize, ACCESS_TOKEN, voices_folder, quantization)
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# use normal whisper
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res = transcriptor.whisper()
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# use faster-whisper (simply faster)
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res = transcriptor.faster_whisper()
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# use a custom trained whisper model
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res = transcriptor.custom_whisper("D:/whisper_tiny_model/tiny.pt")
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# use a huggingface whisper model
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res = transcriptor.huggingface_model("Jingmiao/whisper-small-chinese_base")
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# use assembly ai model
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res = transcriptor.assemby_ai_model("assemblyAI api key")
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res --> [["start", "end", "text", "speaker"], ["start", "end", "text", "speaker"]...]
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```
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#### if you don't want speaker names: keep voices_folder as an empty string ""
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start: starting time of speech in seconds
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end: ending time of speech in seconds
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text: transcribed text for speech during start and end
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speaker: speaker of the text
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supported language codes:
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```
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"af", "am", "ar", "as", "az", "ba", "be", "bg", "bn", "bo", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fo", "fr", "gl", "gu", "ha", "haw", "he", "hi", "hr", "ht", "hu", "hy", "id", "is","it", "ja", "jw", "ka", "kk", "km", "kn", "ko", "la", "lb", "ln", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn","mr", "ms", "mt", "my", "ne", "nl", "nn", "no", "oc", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk","sl", "sn", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "tg", "th", "tk", "tl", "tr", "tt", "uk", "ur", "uz","vi", "yi", "yo", "zh", "yue"
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```
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supported language names:
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```
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"Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Bashkir", "Belarusian", "Bulgarian", "Bengali","Tibetan", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Spanish","Estonian", "Basque", "Persian", "Finnish", "Faroese", "French", "Galician", "Gujarati", "Hausa", "Hawaiian","Hebrew", "Hindi", "Croatian", "Haitian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese","Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Latin", "Luxembourgish", "Lingala", "Lao","Lithuanian", "Latvian", "Malagasy", "Maori", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Maltese","Burmese", "Nepali", "Dutch", "Norwegian Nynorsk", "Norwegian", "Occitan", "Punjabi", "Polish", "Pashto","Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhalese", "Slovak", "Slovenian", "Shona", "Somali","Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Tajik", "Thai", "Turkmen", "Tagalog","Turkish", "Tatar", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Yiddish", "Yoruba", "Chinese", "Cantonese",
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```
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### Audio preprocessing example:
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```
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from speechlib import PreProcessor
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file = "obama1.mp3"
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#initialize
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prep = PreProcessor()
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# convert mp3 to wav
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wav_file = prep.convert_to_wav(file)
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# convert wav file from stereo to mono
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prep.convert_to_mono(wav_file)
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# re-encode wav file to have 16-bit PCM encoding
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prep.re_encode(wav_file)
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```
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### Performance
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```
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These metrics are from Google Colab tests.
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These metrics do not take into account model download times.
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These metrics are done without quantization enabled.
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(quantization will make this even faster)
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metrics for faster-whisper "tiny" model:
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on gpu:
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audio name: obama_zach.wav
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duration: 6 min 36 s
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diarization time: 24s
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speaker recognition time: 10s
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transcription time: 64s
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metrics for faster-whisper "small" model:
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on gpu:
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audio name: obama_zach.wav
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duration: 6 min 36 s
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diarization time: 24s
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speaker recognition time: 10s
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transcription time: 95s
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metrics for faster-whisper "medium" model:
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on gpu:
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audio name: obama_zach.wav
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duration: 6 min 36 s
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diarization time: 24s
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speaker recognition time: 10s
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transcription time: 193s
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metrics for faster-whisper "large" model:
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on gpu:
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audio name: obama_zach.wav
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duration: 6 min 36 s
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diarization time: 24s
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speaker recognition time: 10s
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transcription time: 343s
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```
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