app.py

from typing import Iterator

from io import StringIO
import os
import pathlib
import tempfile

# External programs
import whisper
import ffmpeg

# UI
import gradio as gr

from src.download import ExceededMaximumDuration, downloadUrl
from src.utils import slugify, write_srt, write_vtt
from src.vad import VadPeriodicTranscription, VadSileroTranscription

# Limitations (set to -1 to disable)
DEFAULT_INPUT_AUDIO_MAX_DURATION = 600 # seconds

# Whether or not to automatically delete all uploaded files, to save disk space
DELETE_UPLOADED_FILES = True

# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself 
MAX_FILE_PREFIX_LENGTH = 17

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"
]

model_cache = dict()

class UI:
    def __init__(self, inputAudioMaxDuration):
        self.vad_model = None
        self.inputAudioMaxDuration = inputAudioMaxDuration

    def transcribeFile(self, modelName, languageName, urlData, uploadFile, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding):
        try:
            source, sourceName = self.getSource(urlData, uploadFile, microphoneData)
            
            try:
                selectedLanguage = languageName.lower() if len(languageName) > 0 else None
                selectedModel = modelName if modelName is not None else "base"

                model = model_cache.get(selectedModel, None)
                
                if not model:
                    model = whisper.load_model(selectedModel)
                    model_cache[selectedModel] = model

                # Callable for processing an audio file
                whisperCallable = lambda audio : model.transcribe(audio, language=selectedLanguage, task=task)

                # The results
                if (vad == 'silero-vad'):
                    # Use Silero VAD and include gaps
                    if (self.vad_model is None):
                        self.vad_model = VadSileroTranscription()

                    process_gaps = VadSileroTranscription(transcribe_non_speech = True, 
                                    max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize, 
                                    segment_padding_left=vadPadding, segment_padding_right=vadPadding, copy=self.vad_model)
                    result = process_gaps.transcribe(source, whisperCallable)
                elif (vad == 'silero-vad-skip-gaps'):
                    # Use Silero VAD 
                    if (self.vad_model is None):
                        self.vad_model = VadSileroTranscription()
                        
                    skip_gaps = VadSileroTranscription(transcribe_non_speech = False, 
                                    max_silent_period=vadMergeWindow, max_merge_size=vadMaxMergeSize, 
                                    segment_padding_left=vadPadding, segment_padding_right=vadPadding, copy=self.vad_model)
                    result = skip_gaps.transcribe(source, whisperCallable)
                elif (vad == 'periodic-vad'):
                    # Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
                    # it may create a break in the middle of a sentence, causing some artifacts.
                    periodic_vad = VadPeriodicTranscription(periodic_duration=vadMaxMergeSize)
                    result = periodic_vad.transcribe(source, whisperCallable)
                else:
                    # Default VAD
                    result = whisperCallable(source)

                text = result["text"]

                language = result["language"]
                languageMaxLineWidth = getMaxLineWidth(language)

                print("Max line width " + str(languageMaxLineWidth))
                vtt = getSubs(result["segments"], "vtt", languageMaxLineWidth)
                srt = getSubs(result["segments"], "srt", languageMaxLineWidth)

                # Files that can be downloaded
                downloadDirectory = tempfile.mkdtemp()
                filePrefix = slugify(sourceName, allow_unicode=True)

                download = []
                download.append(createFile(srt, downloadDirectory, filePrefix + "-subs.srt"));
                download.append(createFile(vtt, downloadDirectory, filePrefix + "-subs.vtt"));
                download.append(createFile(text, downloadDirectory, filePrefix + "-transcript.txt"));

                return download, text, vtt

            finally:
                # Cleanup source
                if DELETE_UPLOADED_FILES:
                    print("Deleting source file " + source)
                    os.remove(source)
        
        except ExceededMaximumDuration as e:
            return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"

    def getSource(self, urlData, uploadFile, microphoneData):
        if urlData:
            # Download from YouTube
            source = downloadUrl(urlData, self.inputAudioMaxDuration)
        else:
            # File input
            source = uploadFile if uploadFile is not None else microphoneData

            if self.inputAudioMaxDuration > 0:
                # Calculate audio length
                audioDuration = ffmpeg.probe(source)["format"]["duration"]
            
                if float(audioDuration) > self.inputAudioMaxDuration:
                    raise ExceededMaximumDuration(videoDuration=audioDuration, maxDuration=self.inputAudioMaxDuration, message="Video is too long")

        file_path = pathlib.Path(source)
        sourceName = file_path.stem[:MAX_FILE_PREFIX_LENGTH] + file_path.suffix

        return source, sourceName

def getMaxLineWidth(language: str) -> int:
    if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
        # Chinese characters and kana are wider, so limit line length to 40 characters
        return 40
    else:
        # TODO: Add more languages
        # 80 latin characters should fit on a 1080p/720p screen
        return 80

def createFile(text: str, directory: str, fileName: str) -> str:
    # Write the text to a file
    with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
        file.write(text)

    return file.name

def getSubs(segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
    segmentStream = StringIO()

    if format == 'vtt':
        write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
    elif format == 'srt':
        write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
    else:
        raise Exception("Unknown format " + format)

    segmentStream.seek(0)
    return segmentStream.read()
    

def createUi(inputAudioMaxDuration, share=False, server_name: str = None):
    ui = UI(inputAudioMaxDuration)

    ui_description = "Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
    ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
    ui_description += " as well as speech translation and language identification. "

    ui_description += "\n\n\n\nFor longer audio files (>10 minutes), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."

    if inputAudioMaxDuration > 0:
        ui_description += "\n\n" + "Max audio file length: " + str(inputAudioMaxDuration) + " s"

    ui_article = "Read the [documentation her](https://huggingface.co/spaces/aadnk/whisper-webui/blob/main/docs/options.md)"

    demo = gr.Interface(fn=ui.transcribeFile, description=ui_description, article=ui_article, inputs=[
        gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"], value="medium", label="Model"),
        gr.Dropdown(choices=sorted(LANGUAGES), label="Language"),
        gr.Text(label="URL (YouTube, etc.)"),
        gr.Audio(source="upload", type="filepath", label="Upload Audio"), 
        gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
        gr.Dropdown(choices=["transcribe", "translate"], label="Task"),
        gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "periodic-vad"], label="VAD"),
        gr.Number(label="VAD - Merge Window (s)", precision=0, value=5),
        gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=150),
        gr.Number(label="VAD - Padding (s)", precision=None, value=1)
    ], outputs=[
        gr.File(label="Download"),
        gr.Text(label="Transcription"), 
        gr.Text(label="Segments")
    ])

    demo.launch(share=share, server_name=server_name)   

if __name__ == '__main__':
    createUi(DEFAULT_INPUT_AUDIO_MAX_DURATION)