import os import subprocess import json from datetime import timedelta import tempfile import re import gradio as gr import groq from groq import Groq # setup groq client = Groq(api_key=os.environ.get("Groq_Api_Key")) def handle_groq_error(e, model_name): error_data = e.args[0] if isinstance(error_data, str): # Use regex to extract the JSON part of the string json_match = re.search(r'(\{.*\})', error_data) if json_match: json_str = json_match.group(1) # Ensure the JSON string is well-formed json_str = json_str.replace("'", '"') # Replace single quotes with double quotes error_data = json.loads(json_str) if isinstance(e, groq.AuthenticationError): if isinstance(error_data, dict) and 'error' in error_data and 'message' in error_data['error']: error_message = error_data['error']['message'] raise gr.Error(error_message) elif isinstance(e, groq.RateLimitError): if isinstance(error_data, dict) and 'error' in error_data and 'message' in error_data['error']: error_message = error_data['error']['message'] error_message = re.sub(r'org_[a-zA-Z0-9]+', 'org_(censored)', error_message) # censor org raise gr.Error(error_message) else: raise gr.Error(f"Error during Groq API call: {e}") # language codes for subtitle maker LANGUAGE_CODES = { "English": "en", "Chinese": "zh", "German": "de", "Spanish": "es", "Russian": "ru", "Korean": "ko", "French": "fr", "Japanese": "ja", "Portuguese": "pt", "Turkish": "tr", "Polish": "pl", "Catalan": "ca", "Dutch": "nl", "Arabic": "ar", "Swedish": "sv", "Italian": "it", "Indonesian": "id", "Hindi": "hi", "Finnish": "fi", "Vietnamese": "vi", "Hebrew": "he", "Ukrainian": "uk", "Greek": "el", "Malay": "ms", "Czech": "cs", "Romanian": "ro", "Danish": "da", "Hungarian": "hu", "Tamil": "ta", "Norwegian": "no", "Thai": "th", "Urdu": "ur", "Croatian": "hr", "Bulgarian": "bg", "Lithuanian": "lt", "Latin": "la", "Māori": "mi", "Malayalam": "ml", "Welsh": "cy", "Slovak": "sk", "Telugu": "te", "Persian": "fa", "Latvian": "lv", "Bengali": "bn", "Serbian": "sr", "Azerbaijani": "az", "Slovenian": "sl", "Kannada": "kn", "Estonian": "et", "Macedonian": "mk", "Breton": "br", "Basque": "eu", "Icelandic": "is", "Armenian": "hy", "Nepali": "ne", "Mongolian": "mn", "Bosnian": "bs", "Kazakh": "kk", "Albanian": "sq", "Swahili": "sw", "Galician": "gl", "Marathi": "mr", "Panjabi": "pa", "Sinhala": "si", "Khmer": "km", "Shona": "sn", "Yoruba": "yo", "Somali": "so", "Afrikaans": "af", "Occitan": "oc", "Georgian": "ka", "Belarusian": "be", "Tajik": "tg", "Sindhi": "sd", "Gujarati": "gu", "Amharic": "am", "Yiddish": "yi", "Lao": "lo", "Uzbek": "uz", "Faroese": "fo", "Haitian": "ht", "Pashto": "ps", "Turkmen": "tk", "Norwegian Nynorsk": "nn", "Maltese": "mt", "Sanskrit": "sa", "Luxembourgish": "lb", "Burmese": "my", "Tibetan": "bo", "Tagalog": "tl", "Malagasy": "mg", "Assamese": "as", "Tatar": "tt", "Hawaiian": "haw", "Lingala": "ln", "Hausa": "ha", "Bashkir": "ba", "jw": "jw", "Sundanese": "su", } # helper functions def split_audio(input_file_path, chunk_size_mb): chunk_size = chunk_size_mb * 1024 * 1024 # Convert MB to bytes file_number = 1 chunks = [] with open(input_file_path, 'rb') as f: chunk = f.read(chunk_size) while chunk: chunk_name = f"{os.path.splitext(input_file_path)[0]}_part{file_number:03}.mp3" # Pad file number for correct ordering with open(chunk_name, 'wb') as chunk_file: chunk_file.write(chunk) chunks.append(chunk_name) file_number += 1 chunk = f.read(chunk_size) return chunks def merge_audio(chunks, output_file_path): with open("temp_list.txt", "w") as f: for file in chunks: f.write(f"file '{file}'\n") try: subprocess.run( [ "ffmpeg", "-f", "concat", "-safe", "0", "-i", "temp_list.txt", "-c", "copy", "-y", output_file_path ], check=True ) os.remove("temp_list.txt") for chunk in chunks: os.remove(chunk) except subprocess.CalledProcessError as e: raise gr.Error(f"Error during audio merging: {e}") # Checks file extension, size, and downsamples or splits if needed. ALLOWED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"] MAX_FILE_SIZE_MB = 25 CHUNK_SIZE_MB = 25 def check_file(input_file_path): if not input_file_path: raise gr.Error("Please upload an audio/video file.") file_size_mb = os.path.getsize(input_file_path) / (1024 * 1024) file_extension = input_file_path.split(".")[-1].lower() if file_extension not in ALLOWED_FILE_EXTENSIONS: raise gr.Error(f"Invalid file type (.{file_extension}). Allowed types: {', '.join(ALLOWED_FILE_EXTENSIONS)}") if file_size_mb > MAX_FILE_SIZE_MB: gr.Warning( f"File size too large ({file_size_mb:.2f} MB). Attempting to downsample to 16kHz MP3 128kbps. Maximum size allowed: {MAX_FILE_SIZE_MB} MB" ) output_file_path = os.path.splitext(input_file_path)[0] + "_downsampled.mp3" try: subprocess.run( [ "ffmpeg", "-i", input_file_path, "-ar", "16000", "-ab", "128k", "-ac", "1", "-f", "mp3", "-y", output_file_path, ], check=True ) # Check size after downsampling downsampled_size_mb = os.path.getsize(output_file_path) / (1024 * 1024) if downsampled_size_mb > MAX_FILE_SIZE_MB: gr.Warning(f"File still too large after downsampling ({downsampled_size_mb:.2f} MB). Splitting into {CHUNK_SIZE_MB} MB chunks.") return split_audio(output_file_path, CHUNK_SIZE_MB), "split" return output_file_path, None except subprocess.CalledProcessError as e: raise gr.Error(f"Error during downsampling: {e}") return input_file_path, None # subtitle maker def format_time(seconds): hours = int(seconds // 3600) minutes = int((seconds % 3600) // 60) seconds = int(seconds % 60) milliseconds = int((seconds % 1) * 1000) return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}" def json_to_srt(transcription_json): srt_lines = [] for segment in transcription_json: start_time = format_time(segment['start']) end_time = format_time(segment['end']) text = segment['text'] srt_line = f"{segment['id']+1}\n{start_time} --> {end_time}\n{text}\n" srt_lines.append(srt_line) return '\n'.join(srt_lines) def generate_subtitles(input_file, prompt, language, auto_detect_language, model, include_video, font_selection, font_file, font_color, font_size, outline_thickness, outline_color): input_file_path = input_file processed_path, split_status = check_file(input_file_path) full_srt_content = "" total_duration = 0 segment_id_offset = 0 if split_status == "split": srt_chunks = [] video_chunks = [] for i, chunk_path in enumerate(processed_path): try: with open(chunk_path, "rb") as file: transcription_json_response = client.audio.transcriptions.create( file=(os.path.basename(chunk_path), file.read()), model=model, prompt=prompt, response_format="verbose_json", language=None if auto_detect_language else language, temperature=0.0, ) transcription_json = transcription_json_response.segments # Adjust timestamps and segment IDs for segment in transcription_json: segment['start'] += total_duration segment['end'] += total_duration segment['id'] += segment_id_offset segment_id_offset += len(transcription_json) total_duration += transcription_json[-1]['end'] # Update total duration srt_content = json_to_srt(transcription_json) full_srt_content += srt_content temp_srt_path = f"{os.path.splitext(chunk_path)[0]}.srt" with open(temp_srt_path, "w", encoding="utf-8") as temp_srt_file: temp_srt_file.write(srt_content) temp_srt_file.write("\n") # add a new line at the end of the srt chunk file to fix format when merged srt_chunks.append(temp_srt_path) if include_video and input_file_path.lower().endswith((".mp4", ".webm")): try: output_file_path = chunk_path.replace(os.path.splitext(chunk_path)[1], "_with_subs" + os.path.splitext(chunk_path)[1]) # Handle font selection if font_selection == "Custom Font File" and font_file: font_name = os.path.splitext(os.path.basename(font_file.name))[0] # Get font filename without extension font_dir = os.path.dirname(font_file.name) # Get font directory path elif font_selection == "Custom Font File" and not font_file: font_name = None # Let FFmpeg use its default Arial font_dir = None # No font directory gr.Warning(f"You want to use a Custom Font File, but uploaded none. Using the default Arial font.") elif font_selection == "Arial": font_name = None # Let FFmpeg use its default Arial font_dir = None # No font directory # FFmpeg command subprocess.run( [ "ffmpeg", "-y", "-i", chunk_path, "-vf", f"subtitles={temp_srt_path}:fontsdir={font_dir}:force_style='Fontname={font_name},Fontsize={int(font_size)},PrimaryColour=&H{font_color[1:]}&,OutlineColour=&H{outline_color[1:]}&,BorderStyle={int(outline_thickness)},Outline=1'", "-preset", "fast", output_file_path, ], check=True, ) video_chunks.append(output_file_path) except subprocess.CalledProcessError as e: raise gr.Error(f"Error during subtitle addition: {e}") elif include_video and not input_file_path.lower().endswith((".mp4", ".webm")): gr.Warning(f"You have checked on the 'Include Video with Subtitles', but the input file {input_file_path} isn't a video (.mp4 or .webm). Returning only the SRT File.", duration=15) except groq.AuthenticationError as e: handle_groq_error(e, model) except groq.RateLimitError as e: handle_groq_error(e, model) gr.Warning(f"API limit reached during chunk {i+1}. Returning processed chunks only.") if srt_chunks and video_chunks: merge_audio(video_chunks, 'merged_output_video.mp4') with open('merged_output.srt', 'w', encoding="utf-8") as outfile: for chunk_srt in srt_chunks: with open(chunk_srt, 'r', encoding="utf-8") as infile: outfile.write(infile.read()) return 'merged_output.srt', 'merged_output_video.mp4' else: raise gr.Error("Subtitle generation failed due to API limits.") # Merge SRT chunks final_srt_path = os.path.splitext(input_file_path)[0] + "_final.srt" with open(final_srt_path, 'w', encoding="utf-8") as outfile: for chunk_srt in srt_chunks: with open(chunk_srt, 'r', encoding="utf-8") as infile: outfile.write(infile.read()) # Merge video chunks if video_chunks: merge_audio(video_chunks, 'merged_output_video.mp4') return final_srt_path, 'merged_output_video.mp4' else: return final_srt_path, None else: # Single file processing (no splitting) try: with open(processed_path, "rb") as file: transcription_json_response = client.audio.transcriptions.create( file=(os.path.basename(processed_path), file.read()), model=model, prompt=prompt, response_format="verbose_json", language=None if auto_detect_language else language, temperature=0.0, ) transcription_json = transcription_json_response.segments srt_content = json_to_srt(transcription_json) temp_srt_path = os.path.splitext(input_file_path)[0] + ".srt" with open(temp_srt_path, "w", encoding="utf-8") as temp_srt_file: temp_srt_file.write(srt_content) if include_video and input_file_path.lower().endswith((".mp4", ".webm")): try: output_file_path = input_file_path.replace( os.path.splitext(input_file_path)[1], "_with_subs" + os.path.splitext(input_file_path)[1] ) # Handle font selection if font_selection == "Custom Font File" and font_file: font_name = os.path.splitext(os.path.basename(font_file.name))[0] # Get font filename without extension font_dir = os.path.dirname(font_file.name) # Get font directory path elif font_selection == "Custom Font File" and not font_file: font_name = None # Let FFmpeg use its default Arial font_dir = None # No font directory gr.Warning(f"You want to use a Custom Font File, but uploaded none. Using the default Arial font.") elif font_selection == "Arial": font_name = None # Let FFmpeg use its default Arial font_dir = None # No font directory # FFmpeg command subprocess.run( [ "ffmpeg", "-y", "-i", input_file_path, "-vf", f"subtitles={temp_srt_path}:fontsdir={font_dir}:force_style='FontName={font_name},Fontsize={int(font_size)},PrimaryColour=&H{font_color[1:]}&,OutlineColour=&H{outline_color[1:]}&,BorderStyle={int(outline_thickness)},Outline=1'", "-preset", "fast", output_file_path, ], check=True, ) return temp_srt_path, output_file_path except subprocess.CalledProcessError as e: raise gr.Error(f"Error during subtitle addition: {e}") elif include_video and not input_file_path.lower().endswith((".mp4", ".webm")): gr.Warning(f"You have checked on the 'Include Video with Subtitles', but the input file {input_file_path} isn't a video (.mp4 or .webm). Returning only the SRT File.", duration=15) return temp_srt_path, None except groq.AuthenticationError as e: handle_groq_error(e, model) except groq.RateLimitError as e: handle_groq_error(e, model) except ValueError as e: raise gr.Error(f"Error creating SRT file: {e}") theme = gr.themes.Soft( primary_hue="sky", secondary_hue="blue", neutral_hue="neutral" ).set( border_color_primary='*neutral_300', block_border_width='1px', block_border_width_dark='1px', block_title_border_color='*secondary_100', block_title_border_color_dark='*secondary_200', input_background_fill_focus='*secondary_300', input_border_color='*border_color_primary', input_border_color_focus='*secondary_500', input_border_width='1px', input_border_width_dark='1px', slider_color='*secondary_500', slider_color_dark='*secondary_600' ) css = """ .gradio-container{max-width: 1400px !important} h1{text-align:center} .extra-option { display: none; } .extra-option.visible { display: block; } """ with gr.Blocks(theme=theme, css=css) as interface: gr.Markdown( """ # Fast Subtitle Maker Inference by Groq API If you are having API Rate Limit issues, you can retry later based on the [rate limits](https://console.groq.com/docs/rate-limits) or Duplicate Space with your own API Key

Hugging Face Space by [Nick088](https://linktr.ee/Nick088)
Discord """ ) with gr.Column(): # Input components input_file = gr.File(label="Upload Audio/Video", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS], visible=True) # Model and options model_choice_subtitles = gr.Dropdown(choices=["whisper-large-v3", "whisper-large-v3-turbo", "distil-whisper-large-v3-en"], value="whisper-large-v3-turbo", label="Audio Speech Recogition (ASR) Model", info="'whisper-large-v3' = Multilingual high quality, 'whisper-large-v3-turbo' = Multilingual fast with minimal impact on quality, good balance, 'distil-whisper-large-v3-en' = English only, fastest with also slight impact on quality") transcribe_prompt_subtitles = gr.Textbox(label="Prompt (Optional)", info="Specify any context or spelling corrections.") with gr.Row(): language_subtitles = gr.Dropdown(choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()], value="en", label="Language") auto_detect_language_subtitles = gr.Checkbox(label="Auto Detect Language") # Generate button transcribe_button_subtitles = gr.Button("Generate Subtitles") # Output and settings include_video_option = gr.Checkbox(label="Include Video with Subtitles") gr.Markdown("The SubText Rip (SRT) File, contains the subtitles, you can upload this to any video editing app for adding the subs to your video and also modify/stilyze them") srt_output = gr.File(label="SRT Output File") show_subtitle_settings = gr.Checkbox(label="Show Subtitle Video Settings", visible=False) with gr.Row(visible=False) as subtitle_video_settings: with gr.Column(): font_selection = gr.Radio(["Arial", "Custom Font File"], value="Arial", label="Font Selection", info="Select what font to use") font_file = gr.File(label="Upload Font File (TTF or OTF)", file_types=[".ttf", ".otf"], visible=False) font_color = gr.ColorPicker(label="Font Color", value="#FFFFFF") font_size = gr.Slider(label="Font Size (in pixels)", minimum=10, maximum=60, value=24, step=1) outline_thickness = gr.Slider(label="Outline Thickness", minimum=0, maximum=5, value=1, step=1) outline_color = gr.ColorPicker(label="Outline Color", value="#000000") video_output = gr.Video(label="Output Video with Subtitles", visible=False) # Event bindings # show video output include_video_option.change(lambda include_video: gr.update(visible=include_video), inputs=[include_video_option], outputs=[video_output]) # show video output subs settings checkbox include_video_option.change(lambda include_video: gr.update(visible=include_video), inputs=[include_video_option], outputs=[show_subtitle_settings]) # show video output subs settings show_subtitle_settings.change(lambda show: gr.update(visible=show), inputs=[show_subtitle_settings], outputs=[subtitle_video_settings]) # uncheck show subtitle settings checkbox if include video is unchecked (to make the output subs settings not visible) show_subtitle_settings.change(lambda show, include_video: gr.update(visible=show and include_video), inputs=[show_subtitle_settings, include_video_option], outputs=[show_subtitle_settings]) # show custom font file selection font_selection.change(lambda font_selection: gr.update(visible=font_selection == "Custom Font File"), inputs=[font_selection], outputs=[font_file]) # Update language dropdown based on model selection def update_language_options(model): if model == "distil-whisper-large-v3-en": return gr.update(choices=[("English", "en")], value="en", interactive=False) else: return gr.update(choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()], value="en", interactive=True) model_choice_subtitles.change(fn=update_language_options, inputs=[model_choice_subtitles], outputs=[language_subtitles]) # Modified generate subtitles event transcribe_button_subtitles.click( fn=generate_subtitles, inputs=[ input_file, transcribe_prompt_subtitles, language_subtitles, auto_detect_language_subtitles, model_choice_subtitles, include_video_option, font_selection, font_file, font_color, font_size, outline_thickness, outline_color, ], outputs=[srt_output, video_output], ) interface.launch(share=True)