Spaces:
Running
Running
| import os | |
| import sys | |
| import torch | |
| import logging | |
| from yt_dlp import YoutubeDL | |
| import gradio as gr | |
| import argparse | |
| from audio_separator.separator import Separator | |
| import numpy as np | |
| import librosa | |
| import soundfile as sf | |
| from ensemble import ensemble_files | |
| import shutil | |
| import gradio_client.utils as client_utils | |
| import validators | |
| import matchering as mg | |
| from typing import Any, Optional | |
| import spaces # Added for @spaces.GPU | |
| import gdown | |
| # Add this import if not already present | |
| import yt_dlp | |
| import scipy.io.wavfile # For audio processing | |
| # Logging setup | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Configuration directory | |
| CONFIG_DIR = "/tmp/SESA-Config" | |
| def ensure_config_dir(): | |
| """Ensure the configuration directory exists and is writable.""" | |
| try: | |
| os.makedirs(CONFIG_DIR, exist_ok=True) | |
| logger.info(f"Configuration directory ensured: {CONFIG_DIR}") | |
| except PermissionError as e: | |
| logger.error(f"Failed to create config directory {CONFIG_DIR}: {e}") | |
| raise RuntimeError(f"Cannot create config directory: {e}") | |
| except Exception as e: | |
| logger.error(f"Unexpected error creating config directory {CONFIG_DIR}: {e}") | |
| raise | |
| # Call early in the script | |
| ensure_config_dir() | |
| # Patch gradio_client.utils._json_schema_to_python_type | |
| original_json_schema_to_python_type = client_utils._json_schema_to_python_type | |
| def patched_json_schema_to_python_type(schema: Any, defs: Optional[dict] = None) -> str: | |
| logger.debug(f"Parsing schema: {schema}") | |
| if isinstance(schema, bool): | |
| logger.info("Found boolean schema, returning 'boolean'") | |
| return "boolean" | |
| if not isinstance(schema, dict): | |
| logger.warning(f"Unexpected schema type: {type(schema)}, returning 'Any'") | |
| return "Any" | |
| if "enum" in schema and schema.get("type") == "string": | |
| logger.info(f"Handling enum schema: {schema['enum']}") | |
| return f"Literal[{', '.join(repr(e) for e in schema['enum'])}]" | |
| try: | |
| return original_json_schema_to_python_type(schema, defs) | |
| except client_utils.APIInfoParseError as e: | |
| logger.error(f"Failed to parse schema {schema}: {e}") | |
| return "str" | |
| client_utils._json_schema_to_python_type = patched_json_schema_to_python_type | |
| # Device and autocast setup | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| use_autocast = device == "cuda" | |
| logger.info(f"Using device: {device}") | |
| # ROFORMER_MODELS and OUTPUT_FORMATS | |
| ROFORMER_MODELS = { | |
| "Vocals": { | |
| 'MelBand Roformer | Big Beta 6X by unwa': 'melband_roformer_big_beta6x.ckpt', | |
| 'MelBand Roformer Kim | Big Beta 4 FT by unwa': 'melband_roformer_big_beta4.ckpt', | |
| 'MelBand Roformer Kim | Big Beta 5e FT by unwa': 'melband_roformer_big_beta5e.ckpt', | |
| 'MelBand Roformer | Big Beta 6 by unwa': 'melband_roformer_big_beta6.ckpt', | |
| 'MelBand Roformer | Vocals by Kimberley Jensen': 'vocals_mel_band_roformer.ckpt', | |
| 'MelBand Roformer Kim | FT 3 by unwa': 'mel_band_roformer_kim_ft3_unwa.ckpt', | |
| 'MelBand Roformer Kim | FT by unwa': 'mel_band_roformer_kim_ft_unwa.ckpt', | |
| 'MelBand Roformer Kim | FT 2 by unwa': 'mel_band_roformer_kim_ft2_unwa.ckpt', | |
| 'MelBand Roformer Kim | FT 2 Bleedless by unwa': 'mel_band_roformer_kim_ft2_bleedless_unwa.ckpt', | |
| 'MelBand Roformer | Vocals by becruily': 'mel_band_roformer_vocals_becruily.ckpt', | |
| 'MelBand Roformer | Vocals Fullness by Aname': 'mel_band_roformer_vocal_fullness_aname.ckpt', | |
| 'BS Roformer | Vocals by Gabox': 'bs_roformer_vocals_gabox.ckpt', | |
| 'MelBand Roformer | Vocals by Gabox': 'mel_band_roformer_vocals_gabox.ckpt', | |
| 'MelBand Roformer | Vocals FV1 by Gabox': 'mel_band_roformer_vocals_fv1_gabox.ckpt', | |
| 'MelBand Roformer | Vocals FV2 by Gabox': 'mel_band_roformer_vocals_fv2_gabox.ckpt', | |
| 'MelBand Roformer | Vocals FV3 by Gabox': 'mel_band_roformer_vocals_fv3_gabox.ckpt', | |
| 'MelBand Roformer | Vocals FV4 by Gabox': 'mel_band_roformer_vocals_fv4_gabox.ckpt', | |
| 'BS Roformer | Chorus Male-Female by Sucial': 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt', | |
| 'BS Roformer | Male-Female by aufr33': 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt', | |
| }, | |
| "Instrumentals": { | |
| 'MelBand Roformer | FVX by Gabox': 'mel_band_roformer_instrumental_fvx_gabox.ckpt', | |
| 'MelBand Roformer | INSTV8N by Gabox': 'mel_band_roformer_instrumental_instv8n_gabox.ckpt', | |
| 'MelBand Roformer | INSTV8 by Gabox': 'mel_band_roformer_instrumental_instv8_gabox.ckpt', | |
| 'MelBand Roformer | INSTV7N by Gabox': 'mel_band_roformer_instrumental_instv7n_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Bleedless V3 by Gabox': 'mel_band_roformer_instrumental_bleedless_v3_gabox.ckpt', | |
| 'MelBand Roformer Kim | Inst V1 (E) Plus by Unwa': 'melband_roformer_inst_v1e_plus.ckpt', | |
| 'MelBand Roformer Kim | Inst V1 Plus by Unwa': 'melband_roformer_inst_v1_plus.ckpt', | |
| 'MelBand Roformer Kim | Inst V1 by Unwa': 'melband_roformer_inst_v1.ckpt', | |
| 'MelBand Roformer Kim | Inst V1 (E) by Unwa': 'melband_roformer_inst_v1e.ckpt', | |
| 'MelBand Roformer Kim | Inst V2 by Unwa': 'melband_roformer_inst_v2.ckpt', | |
| 'MelBand Roformer | Instrumental by becruily': 'mel_band_roformer_instrumental_becruily.ckpt', | |
| 'MelBand Roformer | Instrumental by Gabox': 'mel_band_roformer_instrumental_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental 2 by Gabox': 'mel_band_roformer_instrumental_2_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental 3 by Gabox': 'mel_band_roformer_instrumental_3_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Bleedless V1 by Gabox': 'mel_band_roformer_instrumental_bleedless_v1_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Bleedless V2 by Gabox': 'mel_band_roformer_instrumental_bleedless_v2_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Fullness V1 by Gabox': 'mel_band_roformer_instrumental_fullness_v1_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Fullness V2 by Gabox': 'mel_band_roformer_instrumental_fullness_v2_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Fullness V3 by Gabox': 'mel_band_roformer_instrumental_fullness_v3_gabox.ckpt', | |
| 'MelBand Roformer | Instrumental Fullness Noisy V4 by Gabox': 'mel_band_roformer_instrumental_fullness_noise_v4_gabox.ckpt', | |
| 'MelBand Roformer | INSTV5 by Gabox': 'mel_band_roformer_instrumental_instv5_gabox.ckpt', | |
| 'MelBand Roformer | INSTV5N by Gabox': 'mel_band_roformer_instrumental_instv5n_gabox.ckpt', | |
| 'MelBand Roformer | INSTV6 by Gabox': 'mel_band_roformer_instrumental_instv6_gabox.ckpt', | |
| 'MelBand Roformer | INSTV6N by Gabox': 'mel_band_roformer_instrumental_instv6n_gabox.ckpt', | |
| 'MelBand Roformer | INSTV7 by Gabox': 'mel_band_roformer_instrumental_instv7_gabox.ckpt', | |
| }, | |
| "InstVoc Duality": { | |
| 'MelBand Roformer Kim | InstVoc Duality V1 by Unwa': 'melband_roformer_instvoc_duality_v1.ckpt', | |
| 'MelBand Roformer Kim | InstVoc Duality V2 by Unwa': 'melband_roformer_instvox_duality_v2.ckpt', | |
| }, | |
| "De-Reverb": { | |
| 'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt', | |
| 'MelBand Roformer | De-Reverb by anvuew': 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt', | |
| 'MelBand Roformer | De-Reverb Less Aggressive by anvuew': 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt', | |
| 'MelBand Roformer | De-Reverb Mono by anvuew': 'dereverb_mel_band_roformer_mono_anvuew.ckpt', | |
| 'MelBand Roformer | De-Reverb Big by Sucial': 'dereverb_big_mbr_ep_362.ckpt', | |
| 'MelBand Roformer | De-Reverb Super Big by Sucial': 'dereverb_super_big_mbr_ep_346.ckpt', | |
| 'MelBand Roformer | De-Reverb-Echo by Sucial': 'dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt', | |
| 'MelBand Roformer | De-Reverb-Echo V2 by Sucial': 'dereverb-echo_mel_band_roformer_sdr_13.4843_v2.ckpt', | |
| 'MelBand Roformer | De-Reverb-Echo Fused by Sucial': 'dereverb_echo_mbr_fused.ckpt', | |
| }, | |
| "Denoise": { | |
| 'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt', | |
| 'Mel-Roformer-Denoise-Aufr33-Aggr': 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt', | |
| 'MelBand Roformer | Denoise-Debleed by Gabox': 'mel_band_roformer_denoise_debleed_gabox.ckpt', | |
| 'MelBand Roformer | Bleed Suppressor V1 by unwa-97chris': 'mel_band_roformer_bleed_suppressor_v1.ckpt', | |
| }, | |
| "Karaoke": { | |
| 'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', | |
| 'MelBand Roformer | Karaoke by Gabox': 'mel_band_roformer_karaoke_gabox.ckpt', | |
| 'MelBand Roformer | Karaoke by becruily': 'mel_band_roformer_karaoke_becruily.ckpt', | |
| }, | |
| "General Purpose": { | |
| 'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', | |
| 'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', | |
| 'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', | |
| 'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', | |
| 'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt', | |
| 'MelBand Roformer Kim | SYHFT by SYH99999': 'MelBandRoformerSYHFT.ckpt', | |
| 'MelBand Roformer Kim | SYHFT V2 by SYH99999': 'MelBandRoformerSYHFTV2.ckpt', | |
| 'MelBand Roformer Kim | SYHFT V2.5 by SYH99999': 'MelBandRoformerSYHFTV2.5.ckpt', | |
| 'MelBand Roformer Kim | SYHFT V3 by SYH99999': 'MelBandRoformerSYHFTV3Epsilon.ckpt', | |
| 'MelBand Roformer Kim | Big SYHFT V1 by SYH99999': 'MelBandRoformerBigSYHFTV1.ckpt', | |
| 'MelBand Roformer | Aspiration by Sucial': 'aspiration_mel_band_roformer_sdr_18.9845.ckpt', | |
| 'MelBand Roformer | Aspiration Less Aggressive by Sucial': 'aspiration_mel_band_roformer_less_aggr_sdr_18.1201.ckpt', | |
| } | |
| } | |
| OUTPUT_FORMATS = ['wav', 'flac', 'mp3', 'ogg', 'opus', 'm4a', 'aiff', 'ac3'] | |
| # CSS (unchanged from provided version) | |
| CSS = """ | |
| body { | |
| background: linear-gradient(to bottom, rgba(45, 11, 11, 0.9), rgba(0, 0, 0, 0.8)), url('/content/logo.jpg') no-repeat center center fixed; | |
| background-size: cover; | |
| min-height: 100vh; | |
| margin: 0; | |
| padding: 1rem; | |
| font-family: 'Poppins', sans-serif; | |
| color: #C0C0C0; | |
| overflow-x: hidden; | |
| } | |
| .header-text { | |
| text-align: center; | |
| padding: 100px 20px 20px; | |
| color: #ff4040; | |
| font-size: 3rem; | |
| font-weight: 900; | |
| text-shadow: 0 0 10px rgba(255, 64, 64, 0.5); | |
| z-index: 1500; | |
| animation: text-glow 2s infinite; | |
| } | |
| .header-subtitle { | |
| text-align: center; | |
| color: #C0C0C0; | |
| font-size: 1.2rem; | |
| font-weight: 300; | |
| margin-top: -10px; | |
| text-shadow: 0 0 5px rgba(255, 64, 64, 0.3); | |
| } | |
| .gr-tab { | |
| background: rgba(128, 0, 0, 0.5) !important; | |
| border-radius: 12px 12px 0 0 !important; | |
| margin: 0 5px !important; | |
| color: #C0C0C0 !important; | |
| border: 1px solid #ff4040 !important; | |
| z-index: 1500; | |
| transition: background 0.3s ease, color 0.3s ease; | |
| padding: 10px 20px !important; | |
| font-size: 1.1rem !important; | |
| } | |
| button { | |
| transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important; | |
| background: #800000 !important; | |
| border: 1px solid #ff4040 !important; | |
| color: #C0C0C0 !important; | |
| border-radius: 8px !important; | |
| padding: 8px 16px !important; | |
| box-shadow: 0 2px 10px rgba(255, 64, 64, 0.3); | |
| } | |
| button:hover { | |
| transform: scale(1.05) !important; | |
| box-shadow: 0 10px 40px rgba(255, 64, 64, 0.7) !important; | |
| background: #ff4040 !important; | |
| } | |
| .compact-upload.horizontal { | |
| display: inline-flex !important; | |
| align-items: center !important; | |
| gap: 8px !important; | |
| max-width: 400px !important; | |
| height: 40px !important; | |
| padding: 0 12px !important; | |
| border: 1px solid #ff4040 !important; | |
| background: rgba(128, 0, 0, 0.5) !important; | |
| border-radius: 8px !important; | |
| } | |
| .compact-dropdown { | |
| padding: 8px 12px !important; | |
| border-radius: 8px !important; | |
| border: 2px solid #ff6b6b !important; /* Match previous style */ | |
| background: rgba(46, 26, 71, 0.7) !important; /* Match previous style */ | |
| color: #e0e0e0 !important; /* Match previous style */ | |
| width: 100%; | |
| font-size: 1rem !important; | |
| transition: border-color 0.3s ease, box-shadow 0.3s ease !important; | |
| position: relative; | |
| z-index: 100; /* Base z-index for dropdown */ | |
| } | |
| .compact-dropdown:hover { | |
| border-color: #ff8787 !important; /* Lighter shade for hover */ | |
| box-shadow: 0 2px 8px rgba(255, 107, 107, 0.4) !important; | |
| } | |
| .compact-dropdown select, .compact-dropdown .gr-dropdown { | |
| background: transparent !important; | |
| color: #e0e0e0 !important; | |
| border: none !important; | |
| width: 100% !important; | |
| padding: 8px !important; | |
| font-size: 1rem !important; | |
| appearance: none !important; | |
| -webkit-appearance: none !important; | |
| -moz-appearance: none !important; | |
| } | |
| .compact-dropdown .gr-dropdown-menu { | |
| background: rgba(46, 26, 71, 0.95) !important; /* Match previous style */ | |
| border: 2px solid #ff6b6b !important; /* Match previous style */ | |
| border-radius: 8px !important; | |
| color: #e0e0e0 !important; | |
| max-height: 300px !important; | |
| overflow-y: auto !important; | |
| z-index: 300 !important; /* Ensure it stays above other elements */ | |
| width: 100% !important; | |
| opacity: 1 !important; | |
| visibility: visible !important; | |
| position: absolute !important; | |
| top: 100% !important; | |
| left: 0 !important; | |
| pointer-events: auto !important; | |
| } | |
| .compact-dropdown:hover .gr-dropdown-menu { | |
| display: block !important; /* Ensure menu stays open on hover */ | |
| } | |
| .compact-dropdown .gr-dropdown-menu option { | |
| padding: 8px !important; | |
| color: #e0e0e0 !important; | |
| background: transparent !important; | |
| } | |
| .compact-dropdown .gr-dropdown-menu option:hover { | |
| background: rgba(255, 107, 107, 0.3) !important; /* Match hover effect */ | |
| } | |
| #custom-progress { | |
| margin-top: 10px; | |
| padding: 10px; | |
| background: rgba(128, 0, 0, 0.3); | |
| border-radius: 8px; | |
| border: 1px solid #ff4040; | |
| } | |
| #progress-bar { | |
| height: 20px; | |
| background: linear-gradient(to right, #6e8efb, #ff4040); | |
| border-radius: 5px; | |
| transition: width 0.5s ease-in-out; | |
| max-width: 100% !important; | |
| } | |
| .gr-accordion { | |
| background: rgba(128, 0, 0, 0.5) !important; | |
| border-radius: 10px !important; | |
| border: 1px solid #ff4040 !important; | |
| } | |
| .footer { | |
| text-align: center; | |
| padding: 20px; | |
| color: #ff4040; | |
| font-size: 14px; | |
| margin-top: 40px; | |
| background: rgba(128, 0, 0, 0.3); | |
| border-top: 1px solid #ff4040; | |
| } | |
| #log-accordion { | |
| max-height: 400px; | |
| overflow-y: auto; | |
| background: rgba(0, 0, 0, 0.7) !important; | |
| padding: 10px; | |
| border-radius: 8px; | |
| } | |
| @keyframes text-glow { | |
| 0% { text-shadow: 0 0 5px rgba(192, 192, 192, 0); } | |
| 50% { text-shadow: 0 0 15px rgba(192, 192, 192, 1); } | |
| 100% { text-shadow: 0 0 5px rgba(192, 192, 192, 0); } | |
| } | |
| """ | |
| import os | |
| import yt_dlp | |
| import gdown | |
| from scipy.io import wavfile | |
| from pydub import AudioSegment | |
| def download_audio(url, cookie_file=None): | |
| ydl_opts = { | |
| 'format': 'bestaudio[ext=webm]/bestaudio[ext=m4a]/bestaudio[ext=opus]/bestaudio[ext=aac]/bestaudio -video', | |
| 'postprocessors': [{ | |
| 'key': 'FFmpegExtractAudio', | |
| 'preferredcodec': 'wav', | |
| 'preferredquality': '192', | |
| }], | |
| 'outtmpl': 'ytdl/%(title)s.%(ext)s', | |
| 'user_agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36', | |
| 'geo_bypass': True, | |
| 'force_ipv4': True, | |
| 'referer': 'https://www.youtube.com/', | |
| 'noplaylist': True, | |
| 'cookiefile': cookie_file.name if cookie_file else None, | |
| 'extractor_retries': 5, | |
| 'ignoreerrors': False, | |
| 'no_check_certificate': True, | |
| 'verbose': True, | |
| } | |
| # Check if it's a Google Drive link | |
| if 'drive.google.com' in url or 'https://drive.google.com' in url: | |
| try: | |
| # Create the 'ytdl' directory if it doesn't exist | |
| os.makedirs('ytdl', exist_ok=True) | |
| # Extract file ID from the URL | |
| file_id = url.split('/d/')[1].split('/')[0] | |
| download_url = f'https://drive.google.com/uc?id={file_id}' | |
| temp_output_path = 'ytdl/gdrive_temp_audio' # Temporary file for raw download | |
| gdown.download(download_url, temp_output_path, quiet=False) | |
| if not os.path.exists(temp_output_path): | |
| return None, "Downloaded file not found", None | |
| # Convert the downloaded file to WAV using pydub | |
| output_path = 'ytdl/gdrive_audio.wav' | |
| audio = AudioSegment.from_file(temp_output_path) | |
| audio.export(output_path, format="wav") | |
| # Read the converted WAV file | |
| sample_rate, data = wavfile.read(output_path) | |
| audio_data = (sample_rate, data) | |
| # Clean up the temporary file | |
| os.remove(temp_output_path) | |
| return output_path, "Download successful", audio_data | |
| except Exception as e: | |
| return None, f"Google Drive download failed: {str(e)}", None | |
| # Handle YouTube link | |
| else: | |
| os.makedirs('ytdl', exist_ok=True) | |
| with yt_dlp.YoutubeDL(ydl_opts) as ydl: | |
| try: | |
| info_dict = ydl.extract_info(url, download=True) | |
| base_file_path = ydl.prepare_filename(info_dict) | |
| file_path = base_file_path | |
| for ext in ['.webm', '.m4a', '.opus', '.aac']: | |
| file_path = file_path.replace(ext, '.wav') | |
| if not os.path.exists(file_path): | |
| return None, "Downloaded file not found", None | |
| sample_rate, data = wavfile.read(file_path) | |
| audio_data = (sample_rate, data) | |
| return file_path, "Download successful", audio_data | |
| except yt_dlp.utils.ExtractorError as e: | |
| if "Sign in to confirm you’re not a bot" in str(e) or "The provided YouTube account cookies are no longer valid" in str(e): | |
| return None, "Authentication failed. Please upload updated cookies from a logged-in browser session in the respective tab. See https://github.com/yt-dlp/yt-dlp/wiki/Extractors#exporting-youtube-cookies for instructions.", None | |
| elif "HTTP Error 403: Forbidden" in str(e): | |
| return None, "Download failed: HTTP Error 403. This format requires a GVS PO Token, or the cookies are invalid. Please upload fresh cookies. See https://github.com/yt-dlp/yt-dlp/wiki/PO-Token-Guide for advanced troubleshooting.", None | |
| return None, f"Download failed: {str(e)}", None | |
| except Exception as e: | |
| return None, f"Unexpected error: {str(e)}", None | |
| def roformer_separator(audio, model_key, seg_size, override_seg_size, overlap, pitch_shift, model_dir, output_dir, out_format, norm_thresh, amp_thresh, batch_size, exclude_stems="", progress=gr.Progress(track_tqdm=True)): | |
| """Separate audio into stems using a Roformer model.""" | |
| if not audio: | |
| raise ValueError("No audio file provided.") | |
| temp_audio_path = None # Initialize to None to avoid undefined variable in finally | |
| try: | |
| # If audio is a tuple (sample_rate, data), save it as a temporary file | |
| if isinstance(audio, tuple): | |
| sample_rate, data = audio | |
| temp_audio_path = os.path.join("/tmp", "temp_audio.wav") | |
| scipy.io.wavfile.write(temp_audio_path, sample_rate, data) | |
| audio = temp_audio_path | |
| override_seg_size = override_seg_size == "True" | |
| if os.path.exists(output_dir): | |
| shutil.rmtree(output_dir) | |
| os.makedirs(output_dir, exist_ok=True) | |
| base_name = os.path.splitext(os.path.basename(audio))[0] | |
| for category, models in ROFORMER_MODELS.items(): | |
| if model_key in models: | |
| model = models[model_key] | |
| break | |
| else: | |
| raise ValueError(f"Model '{model_key}' not found.") | |
| logger.info(f"Separating {base_name} with {model_key} on {device}") | |
| separator = Separator( | |
| log_level=logging.INFO, | |
| model_file_dir=model_dir, | |
| output_dir=output_dir, | |
| output_format=out_format, | |
| normalization_threshold=norm_thresh, | |
| amplification_threshold=amp_thresh, | |
| use_autocast=use_autocast, | |
| mdxc_params={"segment_size": seg_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, "pitch_shift": pitch_shift} | |
| ) | |
| progress(0.2, desc="Loading model...") | |
| separator.load_model(model_filename=model) | |
| progress(0.7, desc="Separating audio...") | |
| separation = separator.separate(audio) | |
| stems = [os.path.join(output_dir, file_name) for file_name in separation] | |
| if exclude_stems.strip(): | |
| excluded = [s.strip().lower() for s in exclude_stems.split(',')] | |
| filtered_stems = [stem for stem in stems if not any(ex in os.path.basename(stem).lower() for ex in excluded)] | |
| return filtered_stems[0] if filtered_stems else None, filtered_stems[1] if len(filtered_stems) > 1 else None | |
| return stems[0], stems[1] if len(stems) > 1 else None | |
| except Exception as e: | |
| logger.error(f"Separation failed: {e}") | |
| raise RuntimeError(f"Separation failed: {e}") | |
| finally: | |
| # Clean up temporary file if it was created | |
| if temp_audio_path and os.path.exists(temp_audio_path): | |
| try: | |
| os.remove(temp_audio_path) | |
| logger.info(f"Cleaned up temporary file: {temp_audio_path}") | |
| except Exception as e: | |
| logger.warning(f"Failed to clean up temporary file {temp_audio_path}: {e}") | |
| def auto_ensemble_process(audio, model_keys, seg_size=128, overlap=0.1, out_format="wav", use_tta="False", model_dir="/tmp/audio-separator-models/", output_dir="output", norm_thresh=0.9, amp_thresh=0.9, batch_size=1, ensemble_method="avg_wave", exclude_stems="", weights_str=""): | |
| temp_audio_path = None | |
| chunk_paths = [] | |
| try: | |
| if not audio or not model_keys: | |
| raise ValueError("Audio or models missing.") | |
| # Handle tuple input (sample_rate, data) | |
| if isinstance(audio, tuple): | |
| sample_rate, data = audio | |
| temp_audio_path = os.path.join("/tmp", "temp_audio.wav") | |
| scipy.io.wavfile.write(temp_audio_path, sample_rate, data) | |
| audio = temp_audio_path | |
| # Load audio to check duration | |
| audio_data, sr = librosa.load(audio, sr=None, mono=False) | |
| duration = librosa.get_duration(y=audio_data, sr=sr) | |
| logger.info(f"Audio duration: {duration:.2f} seconds") | |
| # Split audio if longer than 15 minutes (900 seconds) | |
| chunk_duration = 300 # 5 minutes in seconds | |
| chunks = [] | |
| if duration > 900: | |
| logger.info(f"Audio exceeds 15 minutes, splitting into {chunk_duration}-second chunks") | |
| num_chunks = int(np.ceil(duration / chunk_duration)) | |
| for i in range(num_chunks): | |
| start = i * chunk_duration * sr | |
| end = min((i + 1) * chunk_duration * sr, audio_data.shape[-1]) | |
| chunk_data = audio_data[:, start:end] if audio_data.ndim == 2 else audio_data[start:end] | |
| chunk_path = os.path.join("/tmp", f"chunk_{i}.wav") | |
| sf.write(chunk_path, chunk_data.T if audio_data.ndim == 2 else chunk_data, sr) | |
| chunks.append(chunk_path) | |
| chunk_paths.append(chunk_path) | |
| logger.info(f"Created chunk {i}: {chunk_path}") | |
| else: | |
| chunks = [audio] | |
| use_tta = use_tta == "True" | |
| # Create output directory | |
| if os.path.exists(output_dir): | |
| shutil.rmtree(output_dir) | |
| os.makedirs(output_dir, exist_ok=True) | |
| base_name = os.path.splitext(os.path.basename(audio))[0] | |
| logger.info(f"Ensemble for {base_name} with {model_keys} on {device}") | |
| all_stems = [] | |
| model_stems = {} # Store stems per model for concatenation | |
| for model_key in model_keys: | |
| model_stems[model_key] = {"vocals": [], "other": []} | |
| for category, models in ROFORMER_MODELS.items(): | |
| if model_key in models: | |
| model = models[model_key] | |
| break | |
| else: | |
| logger.warning(f"Model {model_key} not found, skipping") | |
| continue | |
| for chunk_idx, chunk_path in enumerate(chunks): | |
| separator = Separator( | |
| log_level=logging.INFO, | |
| model_file_dir=model_dir, | |
| output_dir=output_dir, | |
| output_format=out_format, | |
| normalization_threshold=norm_thresh, | |
| amplification_threshold=amp_thresh, | |
| use_autocast=use_autocast, | |
| mdxc_params={"segment_size": seg_size, "overlap": overlap, "use_tta": use_tta, "batch_size": batch_size} | |
| ) | |
| logger.info(f"Loading {model_key} for chunk {chunk_idx}") | |
| separator.load_model(model_filename=model) | |
| logger.info(f"Separating chunk {chunk_idx} with {model_key}") | |
| separation = separator.separate(chunk_path) | |
| stems = [os.path.join(output_dir, file_name) for file_name in separation] | |
| # Store stems for this chunk | |
| for stem in stems: | |
| if "vocals" in os.path.basename(stem).lower(): | |
| model_stems[model_key]["vocals"].append(stem) | |
| elif "other" in os.path.basename(stem).lower(): | |
| model_stems[model_key]["other"].append(stem) | |
| # Clean up memory | |
| separator = None | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| logger.info(f"Cleared CUDA cache after {model_key} chunk {chunk_idx}") | |
| # Combine stems for each model | |
| for model_key, stems_dict in model_stems.items(): | |
| for stem_type in ["vocals", "other"]: | |
| if stems_dict[stem_type]: | |
| combined_path = os.path.join(output_dir, f"{base_name}_{stem_type}_{model_key.replace(' | ', '_').replace(' ', '_')}.wav") | |
| combined_data = [] | |
| for stem_path in stems_dict[stem_type]: | |
| data, _ = librosa.load(stem_path, sr=sr, mono=False) | |
| combined_data.append(data) | |
| combined_data = np.concatenate(combined_data, axis=-1) if combined_data[0].ndim == 2 else np.concatenate(combined_data) | |
| sf.write(combined_path, combined_data.T if combined_data.ndim == 2 else combined_data, sr) | |
| logger.info(f"Combined {stem_type} for {model_key}: {combined_path}") | |
| if exclude_stems.strip() and stem_type.lower() in [s.strip().lower() for s in exclude_stems.split(',')]: | |
| logger.info(f"Excluding {stem_type} for {model_key}") | |
| continue | |
| all_stems.append(combined_path) | |
| if not all_stems: | |
| raise ValueError("No valid stems for ensemble after exclusion.") | |
| # Ensemble the combined stems | |
| weights = [float(w.strip()) for w in weights_str.split(',')] if weights_str.strip() else [1.0] * len(all_stems) | |
| if len(weights) != len(all_stems): | |
| weights = [1.0] * len(all_stems) | |
| output_file = os.path.join(output_dir, f"{base_name}_ensemble_{ensemble_method}.{out_format}") | |
| ensemble_args = [ | |
| "--files", *all_stems, | |
| "--type", ensemble_method, | |
| "--weights", *[str(w) for w in weights], | |
| "--output", output_file | |
| ] | |
| logger.info(f"Running ensemble with args: {ensemble_args}") | |
| ensemble_files(ensemble_args) | |
| logger.info("Ensemble complete") | |
| return output_file, f"Ensemble completed with {ensemble_method}, excluded: {exclude_stems if exclude_stems else 'None'}" | |
| except Exception as e: | |
| logger.error(f"Ensemble failed: {e}") | |
| raise RuntimeError(f"Ensemble failed: {e}") | |
| finally: | |
| # Clean up temporary files | |
| for path in chunk_paths + ([temp_audio_path] if temp_audio_path and os.path.exists(temp_audio_path) else []): | |
| try: | |
| if os.path.exists(path): | |
| os.remove(path) | |
| logger.info(f"Successfully cleaned up {path}") | |
| except Exception as e: | |
| logger.error(f"Failed to clean up {path}: {e}") | |
| def update_roformer_models(category): | |
| """Update Roformer model dropdown based on selected category.""" | |
| choices = list(ROFORMER_MODELS.get(category, {}).keys()) or [] | |
| logger.debug(f"Updating roformer models for category {category}: {choices}") | |
| return gr.update(choices=choices, value=choices[0] if choices else None) | |
| def update_ensemble_models(category): | |
| """Update ensemble model dropdown based on selected category.""" | |
| choices = list(ROFORMER_MODELS.get(category, {}).keys()) or [] | |
| logger.debug(f"Updating ensemble models for category {category}: {choices}") | |
| return gr.update(choices=choices, value=[]) | |
| def download_audio_wrapper(url, cookie_file): | |
| file_path, status, audio_data = download_audio(url, cookie_file) | |
| return audio_data, status # Return audio_data for gr.Audio, status for gr.Textbox | |
| def create_interface(): | |
| with gr.Blocks(title="🎵 SESA Fast Separation 🎵", css=CSS, elem_id="app-container") as app: | |
| gr.Markdown("<h1 class='header-text'>🎵 SESA Fast Separation 🎵</h1>") | |
| gr.Markdown("**Note**: If YouTube downloads fail, try uploading an audio file directly or use a valid cookies file.") | |
| with gr.Tabs(): | |
| # Settings Tab | |
| with gr.Tab("⚙️ Settings"): | |
| with gr.Group(elem_classes="dubbing-theme"): | |
| gr.Markdown("### General Settings") | |
| model_file_dir = gr.Textbox(value="/tmp/audio-separator-models/", label="📂 Model Cache", placeholder="Path to model directory", interactive=True) | |
| output_dir = gr.Textbox(value="output", label="📤 Output Directory", placeholder="Where to save results", interactive=True) | |
| output_format = gr.Dropdown(value="wav", choices=OUTPUT_FORMATS, label="🎶 Output Format", interactive=True) | |
| norm_threshold = gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="🔊 Normalization Threshold", interactive=True) | |
| amp_threshold = gr.Slider(0.1, 1.0, value=0.3, step=0.1, label="📈 Amplification Threshold", interactive=True) | |
| batch_size = gr.Slider(1, 16, value=4, step=1, label="⚡ Batch Size", interactive=True) | |
| # Roformer Tab | |
| with gr.Tab("🎤 Roformer"): | |
| with gr.Group(elem_classes="dubbing-theme"): | |
| gr.Markdown("### Audio Separation") | |
| with gr.Row(): | |
| roformer_audio = gr.Audio(label="🎧 Upload Audio", type="filepath", interactive=True) | |
| url_ro = gr.Textbox(label="🔗 Or Paste URL", placeholder="YouTube or audio URL", interactive=True) | |
| cookies_ro = gr.File(label="🍪 Cookies File", file_types=[".txt"], interactive=True) | |
| download_roformer = gr.Button("⬇️ Download", variant="secondary") | |
| roformer_download_status = gr.Textbox(label="📢 Download Status", interactive=False) # Added status output | |
| roformer_exclude_stems = gr.Textbox(label="🚫 Exclude Stems", placeholder="e.g., vocals, drums (comma-separated)", interactive=True) | |
| with gr.Row(): | |
| roformer_category = gr.Dropdown(label="📚 Category", choices=list(ROFORMER_MODELS.keys()), value="General Purpose", interactive=True) | |
| roformer_model = gr.Dropdown(label="🛠️ Model", choices=list(ROFORMER_MODELS["General Purpose"].keys()), interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| roformer_seg_size = gr.Slider(32, 4000, value=256, step=32, label="📏 Segment Size", interactive=True) | |
| roformer_overlap = gr.Slider(2, 10, value=8, step=1, label="🔄 Overlap", interactive=True) | |
| with gr.Row(): | |
| roformer_pitch_shift = gr.Slider(-12, 12, value=0, step=1, label="🎵 Pitch Shift", interactive=True) | |
| roformer_override_seg_size = gr.Dropdown(choices=["True", "False"], value="False", label="🔧 Override Segment Size", interactive=True) | |
| roformer_button = gr.Button("✂️ Separate Now!", variant="primary") | |
| with gr.Row(): | |
| roformer_stem1 = gr.Audio(label="🎸 Stem 1", type="filepath", interactive=False) | |
| roformer_stem2 = gr.Audio(label="🥁 Stem 2", type="filepath", interactive=False) | |
| # Auto Ensemble Tab | |
| with gr.Tab("🎚️ Auto Ensemble"): | |
| with gr.Group(elem_classes="dubbing-theme"): | |
| gr.Markdown("### Ensemble Processing") | |
| with gr.Row(): | |
| ensemble_audio = gr.Audio(label="🎧 Upload Audio", type="filepath", interactive=True) | |
| url_ensemble = gr.Textbox(label="🔗 Or Paste URL", placeholder="YouTube or audio URL", interactive=True) | |
| cookies_ensemble = gr.File(label="🍪 Cookies File", file_types=[".txt"], interactive=True) | |
| download_ensemble = gr.Button("⬇️ Download", variant="secondary") | |
| ensemble_download_status = gr.Textbox(label="📢 Download Status", interactive=False) # Added status output | |
| ensemble_exclude_stems = gr.Textbox(label="🚫 Exclude Stems", placeholder="e.g., vocals, drums (comma-separated)", interactive=True) | |
| with gr.Row(): | |
| ensemble_category = gr.Dropdown(label="📚 Category", choices=list(ROFORMER_MODELS.keys()), value="Instrumentals", interactive=True) | |
| ensemble_models = gr.Dropdown(label="🛠️ Models", choices=list(ROFORMER_MODELS["Instrumentals"].keys()), multiselect=True, interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| ensemble_seg_size = gr.Slider(32, 4000, value=256, step=32, label="📏 Segment Size", interactive=True) | |
| ensemble_overlap = gr.Slider(2, 10, value=8, step=1, label="🔄 Overlap", interactive=True) | |
| ensemble_use_tta = gr.Dropdown(choices=["True", "False"], value="False", label="🔍 Use TTA", interactive=True) | |
| ensemble_method = gr.Dropdown(label="⚙️ Ensemble Method", choices=['avg_wave', 'median_wave', 'max_wave', 'min_wave', 'avg_fft', 'median_fft', 'max_fft', 'min_fft'], value='avg_wave', interactive=True) | |
| ensemble_weights = gr.Textbox(label="⚖️ Weights", placeholder="e.g., 1.0, 1.0 (comma-separated)", interactive=True) | |
| ensemble_button = gr.Button("🎛️ Run Ensemble!", variant="primary") | |
| ensemble_output = gr.Audio(label="🎶 Ensemble Result", type="filepath", interactive=False) | |
| ensemble_status = gr.Textbox(label="📢 Status", interactive=False) | |
| gr.HTML("<div class='footer'>Powered by Audio-Separator 🌟🎶 | Made with ❤️</div>") | |
| # Event Handlers | |
| roformer_category.change(update_roformer_models, inputs=[roformer_category], outputs=[roformer_model]) | |
| download_roformer.click( | |
| fn=download_audio_wrapper, | |
| inputs=[url_ro, cookies_ro], | |
| outputs=[roformer_audio, roformer_download_status] # Updated to use wrapper and output status | |
| ) | |
| roformer_button.click( | |
| fn=roformer_separator, | |
| inputs=[ | |
| roformer_audio, roformer_model, roformer_seg_size, roformer_override_seg_size, | |
| roformer_overlap, roformer_pitch_shift, model_file_dir, output_dir, | |
| output_format, norm_threshold, amp_threshold, batch_size, roformer_exclude_stems | |
| ], | |
| outputs=[roformer_stem1, roformer_stem2] | |
| ) | |
| ensemble_category.change(update_ensemble_models, inputs=[ensemble_category], outputs=[ensemble_models]) | |
| download_ensemble.click( | |
| fn=download_audio_wrapper, | |
| inputs=[url_ensemble, cookies_ensemble], | |
| outputs=[ensemble_audio, ensemble_download_status] # Updated to use wrapper and output status | |
| ) | |
| ensemble_button.click( | |
| fn=auto_ensemble_process, | |
| inputs=[ | |
| ensemble_audio, ensemble_models, ensemble_seg_size, ensemble_overlap, | |
| output_format, ensemble_use_tta, model_file_dir, output_dir, | |
| norm_threshold, amp_threshold, batch_size, ensemble_method, | |
| ensemble_exclude_stems, ensemble_weights | |
| ], | |
| outputs=[ensemble_output, ensemble_status] | |
| ) | |
| return app | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Music Source Separation Web UI") | |
| parser.add_argument("--port", type=int, default=7860, help="Port to run the UI on") | |
| args = parser.parse_args() | |
| app = create_interface() | |
| try: | |
| app.launch(server_name="0.0.0.0", server_port=args.port, share=True) | |
| except Exception as e: | |
| logger.error(f"Failed to launch app: {e}") | |
| raise | |
| finally: | |
| app.close() |