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| import os | |
| import subprocess | |
| import random | |
| import numpy as np | |
| 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.RateLimitError): | |
| 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) | |
| else: | |
| raise gr.Error(f"Error during Groq API call: {e}") | |
| # llms | |
| MAX_SEED = np.iinfo(np.int32).max | |
| def update_max_tokens(model): | |
| if model in ["llama3-70b-8192", "llama3-8b-8192", "gemma-7b-it", "gemma2-9b-it"]: | |
| return gr.update(maximum=8192) | |
| elif model == "mixtral-8x7b-32768": | |
| return gr.update(maximum=32768) | |
| def create_history_messages(history): | |
| history_messages = [{"role": "user", "content": m[0]} for m in history] | |
| history_messages.extend([{"role": "assistant", "content": m[1]} for m in history]) | |
| return history_messages | |
| def generate_response(prompt, history, model, temperature, max_tokens, top_p, seed): | |
| messages = create_history_messages(history) | |
| messages.append({"role": "user", "content": prompt}) | |
| print(messages) | |
| if seed == 0: | |
| seed = random.randint(1, MAX_SEED) | |
| try: | |
| stream = client.chat.completions.create( | |
| messages=messages, | |
| model=model, | |
| temperature=temperature, | |
| max_tokens=max_tokens, | |
| top_p=top_p, | |
| seed=seed, | |
| stop=None, | |
| stream=True, | |
| ) | |
| response = "" | |
| for chunk in stream: | |
| delta_content = chunk.choices[0].delta.content | |
| if delta_content is not None: | |
| response += delta_content | |
| yield response | |
| return response | |
| except Groq.GroqApiException as e: | |
| handle_groq_error(e, model) | |
| # speech to text | |
| ALLOWED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"] | |
| MAX_FILE_SIZE_MB = 25 | |
| CHUNK_SIZE_MB = 25 | |
| 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", | |
| } | |
| def split_audio(audio_file_path, chunk_size_mb): | |
| chunk_size = chunk_size_mb * 1024 * 1024 # Convert MB to bytes | |
| file_number = 1 | |
| chunks = [] | |
| with open(audio_file_path, 'rb') as f: | |
| chunk = f.read(chunk_size) | |
| while chunk: | |
| chunk_name = f"{os.path.splitext(audio_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. | |
| def check_file(audio_file_path): | |
| if not audio_file_path: | |
| raise gr.Error("Please upload an audio file.") | |
| file_size_mb = os.path.getsize(audio_file_path) / (1024 * 1024) | |
| file_extension = audio_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(audio_file_path)[0] + "_downsampled.mp3" | |
| try: | |
| subprocess.run( | |
| [ | |
| "ffmpeg", | |
| "-i", | |
| audio_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 audio_file_path, None | |
| def transcribe_audio(audio_file_path, model, prompt, language, auto_detect_language): | |
| processed_path, split_status = check_file(audio_file_path) | |
| full_transcription = "" | |
| if split_status == "split": | |
| processed_chunks = [] | |
| for i, chunk_path in enumerate(processed_path): | |
| try: | |
| with open(chunk_path, "rb") as file: | |
| transcription = client.audio.transcriptions.create( | |
| file=(os.path.basename(chunk_path), file.read()), | |
| model=model, | |
| prompt=prompt, | |
| response_format="text", | |
| language=None if auto_detect_language else language, | |
| temperature=0.0, | |
| ) | |
| full_transcription += transcription | |
| processed_chunks.append(chunk_path) | |
| except groq.RateLimitError as e: # Handle rate limit error | |
| handle_groq_error(e, model) | |
| gr.Warning(f"API limit reached during chunk {i+1}. Returning processed chunks only.") | |
| if processed_chunks: | |
| merge_audio(processed_chunks, 'merged_output.mp3') | |
| return full_transcription, 'merged_output.mp3' | |
| else: | |
| return "Transcription failed due to API limits.", None | |
| merge_audio(processed_path, 'merged_output.mp3') | |
| return full_transcription, 'merged_output.mp3' | |
| else: | |
| try: | |
| with open(processed_path, "rb") as file: | |
| transcription = client.audio.transcriptions.create( | |
| file=(os.path.basename(processed_path), file.read()), | |
| model=model, | |
| prompt=prompt, | |
| response_format="text", | |
| language=None if auto_detect_language else language, | |
| temperature=0.0, | |
| ) | |
| return transcription.text, None | |
| except groq.RateLimitError as e: # Handle rate limit error | |
| handle_groq_error(e, model) | |
| def translate_audio(audio_file_path, model, prompt): | |
| processed_path, split_status = check_file(audio_file_path) | |
| full_translation = "" | |
| if split_status == "split": | |
| for chunk_path in processed_path: | |
| try: | |
| with open(chunk_path, "rb") as file: | |
| translation = client.audio.translations.create( | |
| file=(os.path.basename(chunk_path), file.read()), | |
| model=model, | |
| prompt=prompt, | |
| response_format="text", | |
| temperature=0.0, | |
| ) | |
| full_translation += translation | |
| except Groq.GroqApiException as e: | |
| handle_groq_error(e, model) | |
| return f"API limit reached. Partial translation: {full_translation}" | |
| return full_translation | |
| else: | |
| try: | |
| with open(processed_path, "rb") as file: | |
| translation = client.audio.translations.create( | |
| file=(os.path.basename(processed_path), file.read()), | |
| model=model, | |
| prompt=prompt, | |
| response_format="text", | |
| temperature=0.0, | |
| ) | |
| return translation | |
| except Groq.GroqApiException as e: | |
| handle_groq_error(e, model) | |
| with gr.Blocks() as interface: | |
| gr.Markdown( | |
| """ | |
| # Groq API UI | |
| 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 <a href="https://huggingface.co/spaces/Nick088/Fast-Subtitle-Maker?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"> <img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a> with <a href=https://console.groq.com/keys>your own API Key</a> </p> | |
| Hugging Face Space by [Nick088](https://linktr.ee/Nick088) | |
| <br> <a href="https://discord.gg/osai"> <img src="https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge" alt="Discord"> </a> | |
| """ | |
| ) | |
| with gr.Tabs(): | |
| with gr.TabItem("LLMs"): | |
| with gr.Row(): | |
| with gr.Column(scale=1, min_width=250): | |
| model = gr.Dropdown( | |
| choices=[ | |
| "llama3-70b-8192", | |
| "llama3-8b-8192", | |
| "mixtral-8x7b-32768", | |
| "gemma-7b-it", | |
| "gemma2-9b-it", | |
| ], | |
| value="llama3-70b-8192", | |
| label="Model", | |
| ) | |
| temperature = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.01, | |
| value=0.5, | |
| label="Temperature", | |
| info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative.", | |
| ) | |
| max_tokens = gr.Slider( | |
| minimum=1, | |
| maximum=8192, | |
| step=1, | |
| value=4096, | |
| label="Max Tokens", | |
| info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b.", | |
| ) | |
| top_p = gr.Slider( | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.01, | |
| value=0.5, | |
| label="Top P", | |
| info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p.", | |
| ) | |
| seed = gr.Number( | |
| precision=0, value=42, label="Seed", info="A starting point to initiate generation, use 0 for random" | |
| ) | |
| model.change(update_max_tokens, inputs=[model], outputs=max_tokens) | |
| with gr.Column(scale=1, min_width=400): | |
| chatbot = gr.ChatInterface( | |
| fn=generate_response, | |
| chatbot=None, | |
| additional_inputs=[ | |
| model, | |
| temperature, | |
| max_tokens, | |
| top_p, | |
| seed, | |
| ], | |
| ) | |
| model.change(update_max_tokens, inputs=[model], outputs=max_tokens) | |
| with gr.TabItem("Speech To Text"): | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcription"): | |
| gr.Markdown("Transcript audio from files to text!") | |
| with gr.Row(): | |
| audio_input = gr.File( | |
| type="filepath", label="Upload File containing Audio", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS] | |
| ) | |
| model_choice_transcribe = gr.Dropdown( | |
| choices=["whisper-large-v3"], | |
| value="whisper-large-v3", | |
| label="Model", | |
| ) | |
| with gr.Row(): | |
| transcribe_prompt = gr.Textbox( | |
| label="Prompt (Optional)", | |
| info="Specify any context or spelling corrections.", | |
| ) | |
| with gr.Column(): | |
| language = gr.Dropdown( | |
| choices=[(lang, code) for lang, code in LANGUAGE_CODES.items()], | |
| value="en", | |
| label="Language", | |
| ) | |
| auto_detect_language = gr.Checkbox(label="Auto Detect Language") | |
| transcribe_button = gr.Button("Transcribe") | |
| transcription_output = gr.Textbox(label="Transcription") | |
| merged_audio_output = gr.File(label="Merged Audio (if chunked)") | |
| transcribe_button.click( | |
| transcribe_audio, | |
| inputs=[audio_input, model_choice_transcribe, transcribe_prompt, language, auto_detect_language], | |
| outputs=[transcription_output, merged_audio_output], | |
| ) | |
| with gr.TabItem("Translation"): | |
| gr.Markdown("Transcript audio from files and translate them to English text!") | |
| with gr.Row(): | |
| audio_input_translate = gr.File( | |
| type="filepath", label="Upload File containing Audio", file_types=[f".{ext}" for ext in ALLOWED_FILE_EXTENSIONS] | |
| ) | |
| model_choice_translate = gr.Dropdown( | |
| choices=["whisper-large-v3"], | |
| value="whisper-large-v3", | |
| label="Audio Speech Recognition (ASR) Model", | |
| ) | |
| with gr.Row(): | |
| translate_prompt = gr.Textbox( | |
| label="Prompt (Optional)", | |
| info="Specify any context or spelling corrections.", | |
| ) | |
| translate_button = gr.Button("Translate") | |
| translation_output = gr.Textbox(label="Translation") | |
| translate_button.click( | |
| translate_audio, | |
| inputs=[audio_input_translate, model_choice_translate, translate_prompt], | |
| outputs=translation_output, | |
| ) | |
| interface.launch(share=True) |