Metadata-Version: 2.1
Name: speechlib
Version: 1.1.2
Summary: 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.
Home-page: https://github.com/NavodPeiris/speechlib
Author: Navod Peiris
Author-email: navodpeiris1234@gmail.com
License: MIT
Description: ### Run your IDE as administrator
        
        you will get following error if administrator permission is not there:
        
        **OSError: [WinError 1314] A required privilege is not held by the client**
        
        ### Requirements
        
        * Python 3.8 or greater
        
        ### GPU execution
        
        GPU execution needs CUDA 11.  
        
        GPU execution requires the following NVIDIA libraries to be installed:
        
        * [cuBLAS for CUDA 11](https://developer.nvidia.com/cublas)
        * [cuDNN 8 for CUDA 11](https://developer.nvidia.com/cudnn)
        
        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.
        
        ### Google Colab:
        
        on google colab run this to install CUDA dependencies:
        ```
        !apt install libcublas11
        ```
        
        You can see this example [notebook](https://colab.research.google.com/drive/1lpoWrHl5443LSnTG3vJQfTcg9oFiCQSz?usp=sharing)
        
        ### installation:
        ```
        pip install speechlib
        ```
        
        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. âš™ 
        
        This library contains following audio preprocessing functions:
        
        1. convert other audio formats to wav
        
        2. convert stereo wav file to mono
        
        3. re-encode the wav file to have 16-bit PCM encoding
        
        Transcriptor method takes 7 arguments. 
        
        1. file to transcribe
        
        2. log_folder to store transcription
        
        3. language used for transcribing (language code is used)
        
        4. model size ("tiny", "small", "medium", "large", "large-v1", "large-v2", "large-v3")
        
        5. ACCESS_TOKEN: huggingface acccess token (also get permission to access `pyannote/speaker-diarization@2.1`)
        
        6. voices_folder (contains speaker voice samples for speaker recognition)
        
        7. quantization: this determine whether to use int8 quantization or not. Quantization may speed up the process but lower the accuracy.
        
        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.
        
        if voices_folder is not provided then speaker tags will be arbitrary.
        
        log_folder is to store the final transcript as a text file.
        
        transcript will also indicate the timeframe in seconds where each speaker speaks.
        
        ### Transcription example:
        
        ```
        from speechlib import Transcriptor
        
        file = "obama_zach.wav"  # your audio file
        voices_folder = "voices" # voices folder containing voice samples for recognition
        language = "en"          # language code
        log_folder = "logs"      # log folder for storing transcripts
        modelSize = "tiny"     # size of model to be used [tiny, small, medium, large-v1, large-v2, large-v3]
        quantization = False   # setting this 'True' may speed up the process but lower the accuracy
        ACCESS_TOKEN = "your huggingface access token" # get permission to access pyannote/speaker-diarization@2.1 on huggingface
        
        # quantization only works on faster-whisper
        transcriptor = Transcriptor(file, log_folder, language, modelSize, ACCESS_TOKEN, voices_folder, quantization)
        
        # use normal whisper
        res = transcriptor.whisper()
        
        # use faster-whisper (simply faster)
        res = transcriptor.faster_whisper()
        
        res --> [["start", "end", "text", "speaker"], ["start", "end", "text", "speaker"]...]
        ```
        
        #### if you don't want speaker names: keep voices_folder as an empty string ""
        
        start: starting time of speech in seconds  
        end: ending time of speech in seconds  
        text: transcribed text for speech during start and end  
        speaker: speaker of the text 
        
        #### voices folder structure:
        ```
        voices_folder    
        |---> person1      
        |        |---> sample1.wav   
        |        |---> sample2.wav     
        |                ...
        |
        |---> person2  
        |        |---> sample1.wav  
        |        |---> sample2.wav   
        |                ...
        |--> ...  
        ```
        
        supported language codes:  
        
        ```
        "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"
        ```
        
        supported language names:
        
        ```
        "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",
        ```
        
        ### Audio preprocessing example:
        
        ```
        from speechlib import PreProcessor
        
        file = "obama1.mp3"
        #initialize
        prep = PreProcessor()
        # convert mp3 to wav
        wav_file = prep.convert_to_wav(file)   
        
        # convert wav file from stereo to mono
        prep.convert_to_mono(wav_file)
        
        # re-encode wav file to have 16-bit PCM encoding
        prep.re_encode(wav_file)
        ```
        
        ### Performance
        ```
        These metrics are from Google Colab tests.
        These metrics do not take into account model download times.
        These metrics are done without quantization enabled.
        (quantization will make this even faster)
        
        metrics for faster-whisper "tiny" model:
            on gpu:
                audio name: obama_zach.wav
                duration: 6 min 36 s
                diarization time: 24s
                speaker recognition time: 10s
                transcription time: 64s
        
        
        metrics for faster-whisper "small" model:
            on gpu:
                audio name: obama_zach.wav
                duration: 6 min 36 s
                diarization time: 24s
                speaker recognition time: 10s
                transcription time: 95s
        
        
        metrics for faster-whisper "medium" model:
            on gpu:
                audio name: obama_zach.wav
                duration: 6 min 36 s
                diarization time: 24s
                speaker recognition time: 10s
                transcription time: 193s
        
        
        metrics for faster-whisper "large" model:
            on gpu:
                audio name: obama_zach.wav
                duration: 6 min 36 s
                diarization time: 24s
                speaker recognition time: 10s
                transcription time: 343s
        ```
        
        #### why not using pyannote/speaker-diarization-3.1, speechbrain >= 1.0.0, faster-whisper >= 1.0.0:
        
        because older versions give more accurate transcriptions. this was tested.
        
        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/pyannote/speaker-diarization
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
