import gradio as gr import os import time import sys import io import tempfile import subprocess import requests from urllib.parse import urlparse from pydub import AudioSegment import logging import torch import importlib from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline import yt_dlp print(f"Current yt-dlp version: {yt_dlp.version.__version__}") class LogCapture(io.StringIO): def __init__(self, callback): super().__init__() self.callback = callback def write(self, s): super().write(s) self.callback(s) # Set up logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Clone and install faster-whisper from GitHub try: subprocess.run(["git", "clone", "https://github.com/SYSTRAN/faster-whisper.git"], check=True) subprocess.run(["pip", "install", "-e", "./faster-whisper"], check=True) except subprocess.CalledProcessError as e: logging.error(f"Error during faster-whisper installation: {e}") sys.exit(1) sys.path.append("./faster-whisper") from faster_whisper import WhisperModel from faster_whisper.transcribe import BatchedInferencePipeline # Check for CUDA availability device = "cuda:0" if torch.cuda.is_available() else "cpu" logging.info(f"Using device: {device}") def download_audio(url, method_choice, proxy_url, proxy_username, proxy_password): """ Downloads audio from a given URL using the specified method and proxy settings. Args: url (str): The URL of the audio. method_choice (str): The method to use for downloading audio. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: tuple: (path to the downloaded audio file, is_temp_file), or (None, False) if failed. """ parsed_url = urlparse(url) logging.info(f"Downloading audio from URL: {url} using method: {method_choice}") try: if 'youtube.com' in parsed_url.netloc or 'youtu.be' in parsed_url.netloc: audio_file = download_youtube_audio(url, method_choice, proxy_url, proxy_username, proxy_password) if not audio_file: error_msg = f"Failed to download audio from {url} using method {method_choice}. Ensure yt-dlp is up to date." logging.error(error_msg) return None, False elif parsed_url.scheme == 'rtsp': audio_file = download_rtsp_audio(url, proxy_url) if not audio_file: error_msg = f"Failed to download RTSP audio from {url}" logging.error(error_msg) return None, False else: audio_file = download_direct_audio(url, method_choice, proxy_url, proxy_username, proxy_password) if not audio_file: error_msg = f"Failed to download audio from {url} using method {method_choice}" logging.error(error_msg) return None, False return audio_file, True except Exception as e: error_msg = f"Error downloading audio from {url} using method {method_choice}: {str(e)}" logging.error(error_msg) return None, False def download_youtube_audio(url, method_choice, proxy_url, proxy_username, proxy_password): """ Downloads audio from a YouTube URL using the specified method. Args: url (str): The YouTube URL. method_choice (str): The method to use for downloading. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ methods = { 'yt-dlp': yt_dlp_method, 'pytube': pytube_method, } method = methods.get(method_choice, yt_dlp_method) try: logging.info(f"Attempting to download YouTube audio using {method_choice}") return method(url, proxy_url, proxy_username, proxy_password) except Exception as e: logging.error(f"Error downloading using {method_choice}: {str(e)}") return None def yt_dlp_method(url, proxy_url, proxy_username, proxy_password): """ Downloads YouTube audio using yt-dlp and saves it to a temporary file. Args: url (str): The YouTube URL. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info(f"Using yt-dlp {yt_dlp.version.version} method") temp_dir = tempfile.mkdtemp() output_template = os.path.join(temp_dir, '%(id)s.%(ext)s') ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': output_template, 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192', }], 'quiet': False, 'no_warnings': False, 'logger': MyLogger(), # Use a custom logger to capture yt-dlp logs 'progress_hooks': [my_hook], # Hook to capture download progress and errors } if proxy_url and len(proxy_url.strip()) > 0: ydl_opts['proxy'] = proxy_url try: with yt_dlp.YoutubeDL(ydl_opts) as ydl: info = ydl.extract_info(url, download=True) if 'entries' in info: # Can be a playlist or a list of videos info = info['entries'][0] output_file = ydl.prepare_filename(info) output_file = os.path.splitext(output_file)[0] + '.mp3' if os.path.exists(output_file): logging.info(f"Downloaded YouTube audio: {output_file}") return output_file else: error_msg = "yt-dlp did not produce an output file." logging.error(error_msg) return None except Exception as e: logging.error(f"yt-dlp failed to download audio: {str(e)}") return None class MyLogger(object): """ Custom logger for yt-dlp to capture logs and errors. """ def debug(self, msg): logging.debug(msg) def info(self, msg): logging.info(msg) def warning(self, msg): logging.warning(msg) def error(self, msg): logging.error(msg) def my_hook(d): """ Hook function to capture yt-dlp download progress and errors. """ if d['status'] == 'finished': logging.info('Download finished, now converting...') elif d['status'] == 'error': logging.error(f"Download error: {d['filename']}") def pytube_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio from a YouTube URL using pytube and saves it to a temporary file. Args: url (str): The YouTube URL. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using pytube method") from pytube import YouTube try: proxies = None if proxy_url and len(proxy_url.strip()) > 0: proxies = { "http": proxy_url, "https": proxy_url } yt = YouTube(url, proxies=proxies) audio_stream = yt.streams.filter(only_audio=True).first() if audio_stream is None: error_msg = "No audio streams available with pytube." logging.error(error_msg) return None temp_dir = tempfile.mkdtemp() out_file = audio_stream.download(output_path=temp_dir) base, ext = os.path.splitext(out_file) new_file = base + '.mp3' os.rename(out_file, new_file) logging.info(f"Downloaded and converted audio to: {new_file}") return new_file except Exception as e: logging.error(f"pytube failed to download audio: {str(e)}") return None def download_rtsp_audio(url, proxy_url): """ Downloads audio from an RTSP URL using FFmpeg. Args: url (str): The RTSP URL. proxy_url (str): Proxy URL if needed. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using FFmpeg to download RTSP stream") output_file = tempfile.mktemp(suffix='.mp3') command = ['ffmpeg', '-i', url, '-acodec', 'libmp3lame', '-ab', '192k', '-y', output_file] env = os.environ.copy() if proxy_url and len(proxy_url.strip()) > 0: env['http_proxy'] = proxy_url env['https_proxy'] = proxy_url try: subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) logging.info(f"Downloaded RTSP audio to: {output_file}") return output_file except subprocess.CalledProcessError as e: logging.error(f"FFmpeg error: {e.stderr.decode()}") return None except Exception as e: logging.error(f"Error downloading RTSP audio: {str(e)}") return None def download_direct_audio(url, method_choice, proxy_url, proxy_username, proxy_password): """ Downloads audio from a direct URL using the specified method. Args: url (str): The direct URL of the audio file. method_choice (str): The method to use for downloading. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info(f"Downloading direct audio from: {url} using method: {method_choice}") methods = { 'wget': wget_method, 'requests': requests_method, 'yt-dlp': yt_dlp_direct_method, 'ffmpeg': ffmpeg_method, 'aria2': aria2_method, } method = methods.get(method_choice, requests_method) try: audio_file = method(url, proxy_url, proxy_username, proxy_password) if not audio_file or not os.path.exists(audio_file): error_msg = f"Failed to download direct audio from {url} using method {method_choice}" logging.error(error_msg) return None return audio_file except Exception as e: logging.error(f"Error downloading direct audio with {method_choice}: {str(e)}") return None def requests_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio using the requests library. Args: url (str): The URL of the audio file. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ try: proxies = None auth = None if proxy_url and len(proxy_url.strip()) > 0: proxies = { "http": proxy_url, "https": proxy_url } if proxy_username and proxy_password: auth = (proxy_username, proxy_password) response = requests.get(url, stream=True, proxies=proxies, auth=auth) if response.status_code == 200: with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_file: for chunk in response.iter_content(chunk_size=8192): if chunk: temp_file.write(chunk) logging.info(f"Downloaded direct audio to: {temp_file.name}") return temp_file.name else: logging.error(f"Failed to download audio from {url} with status code {response.status_code}") return None except Exception as e: logging.error(f"Error in requests_method: {str(e)}") return None def wget_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio using the wget command-line tool. Args: url (str): The URL of the audio file. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using wget method") output_file = tempfile.mktemp(suffix='.mp3') command = ['wget', '-O', output_file, url] env = os.environ.copy() if proxy_url and len(proxy_url.strip()) > 0: env['http_proxy'] = proxy_url env['https_proxy'] = proxy_url try: subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) logging.info(f"Downloaded audio to: {output_file}") return output_file except subprocess.CalledProcessError as e: logging.error(f"Wget error: {e.stderr.decode()}") return None except Exception as e: logging.error(f"Error in wget_method: {str(e)}") return None def yt_dlp_direct_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio using yt-dlp (supports various protocols and sites). Args: url (str): The URL of the audio or webpage containing audio. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using yt-dlp direct method") output_file = tempfile.mktemp(suffix='.mp3') ydl_opts = { 'format': 'bestaudio/best', 'outtmpl': output_file, 'quiet': True, 'no_warnings': True, 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192', }], } if proxy_url and len(proxy_url.strip()) > 0: ydl_opts['proxy'] = proxy_url try: with yt_dlp.YoutubeDL(ydl_opts) as ydl: ydl.download([url]) logging.info(f"Downloaded audio to: {output_file}") return output_file except Exception as e: logging.error(f"Error in yt_dlp_direct_method: {str(e)}") return None def ffmpeg_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio using FFmpeg. Args: url (str): The URL of the audio file. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using ffmpeg method") output_file = tempfile.mktemp(suffix='.mp3') command = ['ffmpeg', '-i', url, '-vn', '-acodec', 'libmp3lame', '-q:a', '2', output_file] env = os.environ.copy() if proxy_url and len(proxy_url.strip()) > 0: env['http_proxy'] = proxy_url env['https_proxy'] = proxy_url try: subprocess.run(command, check=True, capture_output=True, text=True, env=env) logging.info(f"Downloaded and converted audio to: {output_file}") return output_file except subprocess.CalledProcessError as e: logging.error(f"FFmpeg error: {e.stderr}") return None except Exception as e: logging.error(f"Error in ffmpeg_method: {str(e)}") return None def aria2_method(url, proxy_url, proxy_username, proxy_password): """ Downloads audio using aria2. Args: url (str): The URL of the audio file. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. Returns: str: Path to the downloaded audio file, or None if failed. """ logging.info("Using aria2 method") output_file = tempfile.mktemp(suffix='.mp3') command = ['aria2c', '--split=4', '--max-connection-per-server=4', '--out', output_file, url] if proxy_url and len(proxy_url.strip()) > 0: command.extend(['--all-proxy', proxy_url]) try: subprocess.run(command, check=True, capture_output=True, text=True) logging.info(f"Downloaded audio to: {output_file}") return output_file except subprocess.CalledProcessError as e: logging.error(f"Aria2 error: {e.stderr}") return None except Exception as e: logging.error(f"Error in aria2_method: {str(e)}") return None def trim_audio(audio_path, start_time, end_time): """ Trims an audio file to the specified start and end times. Args: audio_path (str): Path to the audio file. start_time (float): Start time in seconds. end_time (float): End time in seconds. Returns: str: Path to the trimmed audio file. Raises: gr.Error: If invalid start or end times are provided. """ try: logging.info(f"Trimming audio from {start_time} to {end_time}") audio = AudioSegment.from_file(audio_path) audio_duration = len(audio) / 1000 # Duration in seconds # Default start and end times if None start_time = max(0, start_time) if start_time is not None else 0 end_time = min(audio_duration, end_time) if end_time is not None else audio_duration # Validate times if start_time >= end_time: raise gr.Error("End time must be greater than start time.") trimmed_audio = audio[int(start_time * 1000):int(end_time * 1000)] with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio_file: trimmed_audio.export(temp_audio_file.name, format="wav") logging.info(f"Trimmed audio saved to: {temp_audio_file.name}") return temp_audio_file.name except Exception as e: logging.error(f"Error trimming audio: {str(e)}") raise gr.Error(f"Error trimming audio: {str(e)}") def save_transcription(transcription): """ Saves the transcription text to a temporary file. Args: transcription (str): The transcription text. Returns: str: The path to the transcription file. """ with tempfile.NamedTemporaryFile(delete=False, suffix='.txt', mode='w', encoding='utf-8') as temp_file: temp_file.write(transcription) logging.info(f"Transcription saved to: {temp_file.name}") return temp_file.name def get_model_options(pipeline_type): """ Returns a list of model IDs based on the selected pipeline type. Args: pipeline_type (str): The type of pipeline. Returns: list: A list of model IDs. """ if pipeline_type == "faster-batched": return ["cstr/whisper-large-v3-turbo-german-int8_float32","cstr/whisper-large-v3-turbo-int8_float32", "SYSTRAN/faster-whisper-large-v1", "GalaktischeGurke/primeline-whisper-large-v3-german-ct2"] elif pipeline_type == "faster-sequenced": return ["cstr/whisper-large-v3-turbo-german-int8_float32","SYSTRAN/faster-whisper-large-v1", "GalaktischeGurke/primeline-whisper-large-v3-german-ct2"] elif pipeline_type == "transformers": return ["cstr/whisper-large-v3-turbo-german-int8_float32","openai/whisper-large-v3", "openai/whisper-large-v2", "openai/whisper-medium", "openai/whisper-small"] else: return [] # Dictionary to store loaded models loaded_models = {} def transcribe_audio(audio_input, audio_url, proxy_url, proxy_username, proxy_password, pipeline_type, model_id, dtype, batch_size, download_method, start_time=None, end_time=None, verbose=False, include_timecodes=False): """ Transcribes audio from a given source using the specified pipeline and model. Args: audio_input (str): Path to uploaded audio file or recorded audio. audio_url (str): URL of audio. proxy_url (str): Proxy URL if needed. proxy_username (str): Proxy username. proxy_password (str): Proxy password. pipeline_type (str): Type of pipeline to use ('faster-batched', 'faster-sequenced', or 'transformers'). model_id (str): The ID of the model to use. dtype (str): Data type for model computations ('int8', 'float16', or 'float32'). batch_size (int): Batch size for transcription. download_method (str): Method to use for downloading audio. start_time (float, optional): Start time in seconds for trimming audio. end_time (float, optional): End time in seconds for trimming audio. verbose (bool, optional): Whether to output verbose logging. include_timecodes (bool, optional): Whether to include timecodes in the transcription. Yields: Tuple[str, str, str or None]: Metrics and messages, transcription text, path to transcription file. """ try: if verbose: logging.getLogger().setLevel(logging.INFO) else: logging.getLogger().setLevel(logging.WARNING) logging.info(f"Transcription parameters: pipeline_type={pipeline_type}, model_id={model_id}, dtype={dtype}, batch_size={batch_size}, download_method={download_method}") verbose_messages = f"Starting transcription with parameters:\nPipeline Type: {pipeline_type}\nModel ID: {model_id}\nData Type: {dtype}\nBatch Size: {batch_size}\nDownload Method: {download_method}\n" if verbose: yield verbose_messages, "", None # Determine the audio source audio_path = None is_temp_file = False if audio_input is not None and len(audio_input) > 0: # audio_input is a filepath to uploaded or recorded audio audio_path = audio_input is_temp_file = False elif audio_url is not None and len(audio_url.strip()) > 0: # audio_url is provided audio_path, is_temp_file = download_audio(audio_url, download_method, proxy_url, proxy_username, proxy_password) if not audio_path: error_msg = f"Error downloading audio from {audio_url} using method {download_method}. Check logs for details." logging.error(error_msg) yield verbose_messages + error_msg, "", None return else: error_msg = "No audio source provided. Please upload an audio file, record audio, or enter a URL." logging.error(error_msg) yield verbose_messages + error_msg, "", None return # Convert start_time and end_time to float or None start_time = float(start_time) if start_time else None end_time = float(end_time) if end_time else None if start_time is not None or end_time is not None: audio_path = trim_audio(audio_path, start_time, end_time) is_temp_file = True # The trimmed audio is a temporary file verbose_messages += f"Audio trimmed from {start_time} to {end_time}\n" if verbose: yield verbose_messages, "", None # Model caching model_key = (pipeline_type, model_id, dtype) if model_key in loaded_models: model_or_pipeline = loaded_models[model_key] logging.info("Loaded model from cache") else: if pipeline_type == "faster-batched": model = WhisperModel(model_id, device=device, compute_type=dtype) model_or_pipeline = BatchedInferencePipeline(model=model) elif pipeline_type == "faster-sequenced": model_or_pipeline = WhisperModel(model_id, device=device, compute_type=dtype) elif pipeline_type == "transformers": # Adjust torch_dtype based on dtype and device if dtype == "float16" and device == "cpu": torch_dtype = torch.float32 elif dtype == "float16": torch_dtype = torch.float16 else: torch_dtype = torch.float32 model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype ) processor = AutoProcessor.from_pretrained(model_id) model_or_pipeline = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, batch_size=batch_size, return_timestamps=True, device=device, ) else: error_msg = "Invalid pipeline type" logging.error(error_msg) yield verbose_messages + error_msg, "", None return loaded_models[model_key] = model_or_pipeline # Cache the model or pipeline # Perform the transcription start_time_perf = time.time() transcription = "" if pipeline_type == "faster-batched": segments, info = model_or_pipeline.transcribe(audio_path, batch_size=batch_size) # Since segments is a generator, we need to iterate over it to complete transcription segments = list(segments) # Exhaust the generator elif pipeline_type == "faster-sequenced": segments, info = model_or_pipeline.transcribe(audio_path) segments = list(segments) # Exhaust the generator else: result = model_or_pipeline(audio_path) segments = result["chunks"] end_time_perf = time.time() # Calculate metrics transcription_time = end_time_perf - start_time_perf audio_file_size = os.path.getsize(audio_path) / (1024 * 1024) metrics_output = ( f"Transcription time: {transcription_time:.2f} seconds\n" f"Audio file size: {audio_file_size:.2f} MB\n" ) if verbose: yield verbose_messages + metrics_output, "", None # Compile the transcription text for segment in segments: if pipeline_type in ["faster-batched", "faster-sequenced"]: if include_timecodes: transcription_segment = f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\n" else: transcription_segment = f"{segment.text}\n" else: if include_timecodes: transcription_segment = f"[{segment['timestamp'][0]:.2f}s -> {segment['timestamp'][1]:.2f}s] {segment['text']}\n" else: transcription_segment = f"{segment['text']}\n" transcription += transcription_segment if verbose: yield verbose_messages + metrics_output, transcription, None # Save the transcription to a file transcription_file = save_transcription(transcription) yield verbose_messages + metrics_output, transcription, transcription_file except Exception as e: error_msg = f"An error occurred during transcription: {str(e)}" logging.error(error_msg) yield verbose_messages + error_msg, "", None finally: # Clean up temporary audio files if audio_path and is_temp_file and os.path.exists(audio_path): os.remove(audio_path) with gr.Blocks() as iface: gr.Markdown("# Audio Transcription") gr.Markdown("Transcribe audio using multiple pipelines and (Faster) Whisper models.") with gr.Row(): audio_input = gr.Audio(label="Upload or Record Audio", sources=["upload", "microphone"], type="filepath") audio_url = gr.Textbox(label="Or Enter URL of audio file or YouTube link") transcribe_button = gr.Button("Transcribe") with gr.Accordion("Advanced Options", open=False): with gr.Row(): proxy_url = gr.Textbox(label="Proxy URL", placeholder="Enter proxy URL if needed", value="", lines=1) proxy_username = gr.Textbox(label="Proxy Username", placeholder="Proxy username (optional)", value="", lines=1) proxy_password = gr.Textbox(label="Proxy Password", placeholder="Proxy password (optional)", value="", lines=1, type="password") with gr.Row(): pipeline_type = gr.Dropdown( choices=["faster-batched", "faster-sequenced", "transformers"], label="Pipeline Type", value="faster-batched" ) model_id = gr.Dropdown( label="Model", choices=get_model_options("faster-batched"), value="cstr/whisper-large-v3-turbo-int8_float32" ) with gr.Row(): dtype = gr.Dropdown(choices=["int8", "float16", "float32"], label="Data Type", value="int8") batch_size = gr.Slider(minimum=1, maximum=32, step=1, value=16, label="Batch Size") download_method = gr.Dropdown( choices=["yt-dlp", "pytube", "youtube-dl", "yt-dlp-alt", "ffmpeg", "aria2", "wget"], label="Download Method", value="yt-dlp" ) with gr.Row(): start_time = gr.Number(label="Start Time (seconds)", value=None, minimum=0) end_time = gr.Number(label="End Time (seconds)", value=None, minimum=0) verbose = gr.Checkbox(label="Verbose Output", value=False) include_timecodes = gr.Checkbox(label="Include timecodes in transcription", value=False) with gr.Row(): metrics_output = gr.Textbox(label="Transcription Metrics and Verbose Messages", lines=10) transcription_output = gr.Textbox(label="Transcription", lines=10) transcription_file = gr.File(label="Download Transcription") def update_model_dropdown(pipeline_type): """ Updates the model dropdown choices based on the selected pipeline type. Args: pipeline_type (str): The selected pipeline type. Returns: gr.update: Updated model dropdown component. """ try: model_choices = get_model_options(pipeline_type) logging.info(f"Model choices for {pipeline_type}: {model_choices}") if model_choices: return gr.update(choices=model_choices, value=model_choices[0], visible=True) else: return gr.update(choices=["No models available"], value=None, visible=False) except Exception as e: logging.error(f"Error in update_model_dropdown: {str(e)}") return gr.update(choices=["Error"], value="Error", visible=True) # Event handler for pipeline_type change pipeline_type.change(update_model_dropdown, inputs=[pipeline_type], outputs=[model_id]) def transcribe_with_progress(*args): # The audio_input is now the first argument for result in transcribe_audio(*args): yield result transcribe_button.click( transcribe_with_progress, inputs=[audio_input, audio_url, proxy_url, proxy_username, proxy_password, pipeline_type, model_id, dtype, batch_size, download_method, start_time, end_time, verbose, include_timecodes], outputs=[metrics_output, transcription_output, transcription_file] ) gr.Examples( examples=[ [None, "https://www.youtube.com/watch?v=daQ_hqA6HDo", "", "", "", "faster-batched", "cstr/whisper-large-v3-turbo-int8_float32", "int8", 16, "yt-dlp", None, None, False, False], [None, "https://mcdn.podbean.com/mf/web/dir5wty678b6g4vg/HoP_453.mp3", "", "", "", "faster-sequenced", "SYSTRAN/faster-whisper-large-v1", "float16", 1, "ffmpeg", 0, 300, False, False], ], inputs=[audio_input, audio_url, proxy_url, proxy_username, proxy_password, pipeline_type, model_id, dtype, batch_size, download_method, start_time, end_time, verbose, include_timecodes], ) iface.launch(share=False, debug=True)