From 0978064c4787c2c623a7ad30c2f6251387781f82 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andrew=20Beveridge=20=E2=98=84=EF=B8=8F?= Date: Tue, 17 Dec 2024 01:40:16 -0500 Subject: [PATCH] Begin adding evaluation scores for all models, initially MUSEDB18 + model fix (#169) * Added initial attempt at script to evaluate model performance using MUSDB18 dataset and evaluation tooling * Added loading of existing raw scores * WIP combined scores * Updated script to process all tracks for all models, and evaluate SDR for all stems supported by MUSDB18. Added combined scores calculated so far. * Added median score calculation, changed inner/outer loop for tracks vs. model processing, renamed accompaniment * Fixed UVR-De-Reverb-aufr33-jarredou.pth model by adding new model-data dict appended to the UVR one --- .gitignore | 2 + audio_separator/model-data.json | 14 + audio_separator/models-scores.json | 5288 +++++++++++++++++ audio_separator/separator/common_separator.py | 11 +- audio_separator/separator/separator.py | 92 +- pyproject.toml | 2 +- tests/model-metrics/test-all-models.py | 225 + 7 files changed, 5550 insertions(+), 84 deletions(-) create mode 100644 audio_separator/model-data.json create mode 100644 audio_separator/models-scores.json create mode 100644 tests/model-metrics/test-all-models.py diff --git a/.gitignore b/.gitignore index 23d1136..977e5ad 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,8 @@ *.wav *.flac *.mp3 +tests/model-metrics/results +tests/model-metrics/datasets # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/audio_separator/model-data.json b/audio_separator/model-data.json new file mode 100644 index 0000000..eae9cc8 --- /dev/null +++ b/audio_separator/model-data.json @@ -0,0 +1,14 @@ +{ + "vr_model_data": { + "97dc361a7a88b2c4542f68364b32c7f6": { + "vr_model_param": "4band_v4_ms_fullband", + "primary_stem": "Dry", + "nout": 32, + "nout_lstm": 128, + "is_karaoke": false, + "is_bv_model": false, + "is_bv_model_rebalanced": 0.0 + } + }, + "mdx_model_data": {} +} diff --git a/audio_separator/models-scores.json b/audio_separator/models-scores.json new file mode 100644 index 0000000..bfc4771 --- /dev/null +++ b/audio_separator/models-scores.json @@ -0,0 +1,5288 @@ +{ + "1_HP-UVR.pth": { + "model_name": "VR Arch Single Model v5: 1_HP-UVR", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 9.86522, + "SIR": 23.3587, + "SAR": 10.1785, + "ISR": 15.0739 + }, + "instrumental": { + "SDR": 15.7431, + "SIR": 20.9606, + "SAR": 17.3032, + "ISR": 29.0623 + } + } + }, + { + "track_name": "A Classic Education - NightOwl", + "scores": { + "vocals": { + "SDR": 5.24871, + "SIR": 15.4624, + "SAR": 4.83391, + "ISR": 8.8857 + }, + "instrumental": { + "SDR": 14.8927, + "SIR": 19.3158, + "SAR": 18.24, + "ISR": 18.8107 + } + } + }, + { + "track_name": "ANiMAL - Clinic A", + "scores": { + "vocals": { + "SDR": 5.43096, + "SIR": 12.0094, + "SAR": 5.75084, + "ISR": 12.7187 + }, + "instrumental": { + "SDR": 10.6068, + "SIR": 18.0944, + "SAR": 12.3348, + "ISR": 14.9249 + } + } + }, + { + "track_name": "ANiMAL - Easy Tiger", + "scores": { + "vocals": { + "SDR": 8.37912, + "SIR": 18.9104, + "SAR": 8.54122, + "ISR": 15.3526 + }, + "instrumental": { + "SDR": 15.3357, + "SIR": 21.73, + "SAR": 16.6099, + "ISR": 21.9107 + } + } + }, + { + "track_name": "ANiMAL - Rockshow", + "scores": { + "vocals": { + "SDR": 2.68601, + "SIR": 5.35433, + "SAR": 4.00494, + "ISR": 12.7504 + }, + "instrumental": { + "SDR": 11.3974, + "SIR": 21.6121, + "SAR": 13.2817, + "ISR": 15.9813 + } + } + }, + { + "track_name": "Actions - Devil's Words", + "scores": { + "vocals": { + "SDR": 9.77937, + "SIR": 20.2537, + "SAR": 9.60245, + "ISR": 13.3208 + }, + "instrumental": { + "SDR": 12.781, + "SIR": 15.3895, + "SAR": 13.3742, + "ISR": 23.1482 + } + } + }, + { + "track_name": "Actions - One Minute Smile", + "scores": { + "vocals": { + "SDR": 8.84219, + "SIR": 16.1098, + "SAR": 9.47788, + "ISR": 13.4743 + }, + "instrumental": { + "SDR": 11.0084, + "SIR": 15.7127, + "SAR": 12.9399, + "ISR": 19.7754 + } + } + }, + { + "track_name": "Actions - South Of The Water", + "scores": { + "vocals": { + "SDR": 9.73747, + "SIR": 18.9403, + "SAR": 10.333, + "ISR": 14.4066 + }, + "instrumental": { + "SDR": 14.318, + "SIR": 18.8837, + "SAR": 15.8694, + "ISR": 22.3595 + } + } + }, + { + "track_name": "Aimee Norwich - 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Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 10.8156, + "SIR": 20.6419, + "SAR": 11.335, + "ISR": 15.3117 + }, + "instrumental": { + "SDR": 14.9986, + "SIR": 25.9346, + "SAR": 17.6734, + "ISR": 17.6449 + } + } + } + ] + }, + "UVR-MDX-NET_Inst_90_beta.onnx": { + "model_name": "MDX-Net Model VIP: UVR-MDX-NET_Inst_90_beta", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 10.6498, + "SIR": 17.5452, + "SAR": 11.721, + "ISR": 15.5642 + }, + "instrumental": { + "SDR": 14.3123, + "SIR": 26.4022, + "SAR": 17.7831, + "ISR": 16.3741 + } + } + } + ] + }, + "UVR-MDX-NET_Inst_187_beta.onnx": { + "model_name": "MDX-Net Model VIP: UVR-MDX-NET_Inst_187_beta", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 11.1344, + "SIR": 17.9928, + "SAR": 12.2004, + "ISR": 15.9915 + }, + "instrumental": { + "SDR": 14.5594, + "SIR": 27.1713, + "SAR": 18.1641, + "ISR": 16.4753 + } + } + } + ] + }, + "UVR-MDX-NET-Inst_full_292.onnx": { + "model_name": "MDX-Net Model VIP: UVR-MDX-NET-Inst_full_292", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 11.135, + "SIR": 21.807, + "SAR": 11.5208, + "ISR": 16.0342 + }, + "instrumental": { + "SDR": 15.3967, + "SIR": 27.4994, + "SAR": 17.8466, + "ISR": 18.241 + } + } + } + ] + }, + "htdemucs_ft.yaml": { + "model_name": "Demucs v4: htdemucs_ft", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 11.2685, + "SIR": 21.257, + "SAR": 11.0359, + "ISR": 19.3753 + }, + "drums": { + "SDR": 13.235, + "SIR": 23.3053, + "SAR": 13.0313, + "ISR": 17.2889 + }, + "bass": { + "SDR": 9.72743, + "SIR": 19.5435, + "SAR": 9.20801, + "ISR": 13.5037 + }, + "other": { + "SDR": 7.25411, + "SIR": 10.8728, + "SAR": 7.31316, + "ISR": 14.1944 + } + } + } + ] + }, + "htdemucs.yaml": { + "model_name": "Demucs v4: htdemucs", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 11.0095, + "SIR": 20.2894, + "SAR": 10.7492, + "ISR": 19.1796 + }, + "drums": { + "SDR": 13.011, + "SIR": 22.7843, + "SAR": 12.9744, + "ISR": 17.4674 + }, + "bass": { + "SDR": 9.17211, + "SIR": 18.7906, + "SAR": 8.75809, + "ISR": 13.0067 + }, + "other": { + "SDR": 7.16063, + "SIR": 10.8834, + "SAR": 6.9937, + "ISR": 14.244 + } + } + } + ] + }, + "hdemucs_mmi.yaml": { + "model_name": "Demucs v4: hdemucs_mmi", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 11.1687, + "SIR": 20.9333, + "SAR": 11.0289, + "ISR": 19.1138 + }, + "drums": { + "SDR": 12.4217, + "SIR": 23.2568, + "SAR": 12.3392, + "ISR": 16.7341 + }, + "bass": { + "SDR": 9.1504, + "SIR": 18.4985, + "SAR": 8.87331, + "ISR": 13.3686 + }, + "other": { + "SDR": 7.17736, + "SIR": 10.6928, + "SAR": 7.28959, + "ISR": 14.5021 + } + } + } + ] + }, + "htdemucs_6s.yaml": { + "model_name": "Demucs v4: htdemucs_6s", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 10.8446, + "SIR": 19.4989, + "SAR": 10.6463, + "ISR": 19.1945 + }, + "drums": { + "SDR": 12.8115, + "SIR": 22.638, + "SAR": 12.997, + "ISR": 16.9512 + }, + "bass": { + "SDR": 9.15013, + "SIR": 16.2437, + "SAR": 8.85638, + "ISR": 13.1591 + }, + "other": { + "SDR": 0.00053, + "SIR": 11.782, + "SAR": 0.00118, + "ISR": 0.2268 + } + } + } + ] + }, + "MDX23C-8KFFT-InstVoc_HQ.ckpt": { + "model_name": "MDX23C Model: MDX23C-InstVoc HQ", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.7312, + "SIR": 29.3476, + "SAR": 13.4577, + "ISR": 16.095 + }, + "instrumental": { + "SDR": 16.8081, + "SIR": 27.6401, + "SAR": 19.9845, + "ISR": 19.3202 + } + } + } + ] + }, + "MDX23C_D1581.ckpt": { + "model_name": "MDX23C Model VIP: MDX23C_D1581", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.0391, + "SIR": 27.4449, + "SAR": 12.1722, + "ISR": 14.8268 + }, + "instrumental": { + "SDR": 16.3295, + "SIR": 24.8897, + "SAR": 18.9426, + "ISR": 19.1322 + } + } + } + ] + }, + "MDX23C-8KFFT-InstVoc_HQ_2.ckpt": { + "model_name": "MDX23C Model VIP: MDX23C-InstVoc HQ 2", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.7508, + "SIR": 29.9531, + "SAR": 13.3726, + "ISR": 16.0716 + }, + "instrumental": { + "SDR": 16.9593, + "SIR": 27.5912, + "SAR": 19.9074, + "ISR": 19.5809 + } + } + } + ] + }, + "model_bs_roformer_ep_317_sdr_12.9755.ckpt": { + "model_name": "Roformer Model: BS-Roformer-Viperx-1297", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 13.5735, + "SIR": 30.32, + "SAR": 14.0441, + "ISR": 17.2849 + }, + "instrumental": { + "SDR": 17.2905, + "SIR": 30.0163, + "SAR": 20.6951, + "ISR": 19.3823 + } + } + } + ] + }, + "model_bs_roformer_ep_368_sdr_12.9628.ckpt": { + "model_name": "Roformer Model: BS-Roformer-Viperx-1296", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 13.5542, + "SIR": 30.4519, + "SAR": 14.0864, + "ISR": 17.2478 + }, + "instrumental": { + "SDR": 17.3035, + "SIR": 30.0666, + "SAR": 20.6723, + "ISR": 19.3953 + } + } + } + ] + }, + "model_bs_roformer_ep_937_sdr_10.5309.ckpt": { + "model_name": "Roformer Model: BS-Roformer-Viperx-1053", + "track_scores": [ + null + ] + }, + "model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt": { + "model_name": "Roformer Model: Mel-Roformer-Viperx-1143", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.3425, + "SIR": 29.3362, + "SAR": 13.0711, + "ISR": 16.2842 + }, + "instrumental": { + "SDR": 16.4826, + "SIR": 27.7054, + "SAR": 19.3585, + "ISR": 19.247 + } + } + } + ] + }, + "MDX23C-De-Reverb-aufr33-jarredou.ckpt": { + "model_name": "MDX23C Model: MDX23C De-Reverb by aufr33-jarredou", + "track_scores": [] + }, + "mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt": { + "model_name": "Roformer Model: Mel-Roformer-Karaoke-Aufr33-Viperx", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.883, + "SIR": 30.7823, + "SAR": 13.354, + "ISR": 14.825 + }, + "instrumental": { + "SDR": 17.1356, + "SIR": 24.909, + "SAR": 20.1133, + "ISR": 19.4389 + } + } + } + ] + }, + "denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt": { + "model_name": "Roformer Model: Mel-Roformer-Denoise-Aufr33", + "track_scores": [] + }, + "denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt": { + "model_name": "Roformer Model: Mel-Roformer-Denoise-Aufr33-Aggr", + "track_scores": [] + }, + "mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt": { + "model_name": "Roformer Model: Mel-Roformer-Crowd-Aufr33-Viperx", + "track_scores": [] + }, + "deverb_bs_roformer_8_384dim_10depth.ckpt": { + "model_name": "Roformer Model: BS-Roformer-De-Reverb", + "track_scores": [ + null + ] + }, + "vocals_mel_band_roformer.ckpt": { + "model_name": "Roformer Model: MelBand Roformer | Vocals by Kimberley Jensen", + "track_scores": [] + }, + "mel_band_roformer_kim_ft_unwa.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | FT by unwa", + "track_scores": [] + }, + "melband_roformer_inst_v1e.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | Inst V1 (E) by Unwa", + "track_scores": [ + { + "track_name": "Moosmusic - Big Dummy Shake", + "scores": { + "vocals": { + "SDR": 12.0889, + "SIR": 30.0273, + "SAR": 12.7786, + "ISR": 14.9145 + }, + "instrumental": { + "SDR": 16.631, + "SIR": 25.3515, + "SAR": 19.4912, + "ISR": 19.3698 + } + } + } + ] + }, + "dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt": { + "model_name": "Roformer Model: MelBand Roformer | De-Reverb by anvuew", + "track_scores": [ + null + ] + }, + "dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt": { + "model_name": "Roformer Model: MelBand Roformer | De-Reverb Less Aggressive by anvuew", + "track_scores": [ + null + ] + }, + "dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt": { + "model_name": "Roformer Model: MelBand Roformer | De-Reverb-Echo by Sucial", + "track_scores": [ + null + ] + }, + "dereverb-echo_mel_band_roformer_sdr_13.4843_v2.ckpt": { + "model_name": "Roformer Model: MelBand Roformer | De-Reverb-Echo V2 by Sucial", + "track_scores": [ + null + ] + }, + "MelBandRoformerSYHFT.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | SYHFT by SYH99999", + "track_scores": [] + }, + "MelBandRoformerSYHFTV2.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | SYHFT V2 by SYH99999", + "track_scores": [] + }, + "MelBandRoformerSYHFTV2.5.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | SYHFT V2.5 by SYH99999", + "track_scores": [] + }, + "MelBandRoformerSYHFTV3Epsilon.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | SYHFT V3 by SYH99999", + "track_scores": [] + }, + "MelBandRoformerBigSYHFTV1.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | Big SYHFT V1 by SYH99999", + "track_scores": [] + }, + "melband_roformer_big_beta4.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | Big Beta 4 FT by unwa", + "track_scores": [] + }, + "melband_roformer_big_beta5e.ckpt": { + "model_name": "Roformer Model: MelBand Roformer Kim | Big Beta 5e FT by unwa", + "track_scores": [] + }, + "model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt": { + "model_name": "Roformer Model: BS Roformer | Chorus Male-Female by Sucial", + "track_scores": [ + null + ] + } +} \ No newline at end of file diff --git a/audio_separator/separator/common_separator.py b/audio_separator/separator/common_separator.py index d243163..095fa75 100644 --- a/audio_separator/separator/common_separator.py +++ b/audio_separator/separator/common_separator.py @@ -377,7 +377,10 @@ def get_stem_output_path(self, stem_name, custom_output_names): """ Gets the output path for a stem based on the stem name and custom output names. """ - if custom_output_names and stem_name in custom_output_names: - return os.path.join(f"{custom_output_names[stem_name]}.{self.output_format.lower()}") - else: - return os.path.join(f"{self.audio_file_base}_({stem_name})_{self.model_name}.{self.output_format.lower()}") + # Convert custom_output_names keys to lowercase for case-insensitive comparison + if custom_output_names: + custom_output_names_lower = {k.lower(): v for k, v in custom_output_names.items()} + if stem_name.lower() in custom_output_names_lower: + return os.path.join(f"{custom_output_names_lower[stem_name.lower()]}.{self.output_format.lower()}") + + return os.path.join(f"{self.audio_file_base}_({stem_name})_{self.model_name}.{self.output_format.lower()}") diff --git a/audio_separator/separator/separator.py b/audio_separator/separator/separator.py index 0cbcb63..125a3ef 100644 --- a/audio_separator/separator/separator.py +++ b/audio_separator/separator/separator.py @@ -67,7 +67,7 @@ class Separator: shifts: 2 overlap: 0.25 segments_enabled: True - + MDXC Architecture Specific Attributes & Defaults: segment_size: 256 override_model_segment_size: False @@ -140,7 +140,7 @@ def __init__( self.normalization_threshold = normalization_threshold if normalization_threshold <= 0 or normalization_threshold > 1: raise ValueError("The normalization_threshold must be greater than 0 and less than or equal to 1.") - + self.amplification_threshold = amplification_threshold if amplification_threshold < 0 or amplification_threshold > 1: raise ValueError("The amplification_threshold must be greater than or equal to 0 and less than or equal to 1.") @@ -161,7 +161,7 @@ def __init__( raise ValueError(f"The sample rate setting is {self.sample_rate}. Enter something less ambitious.") except ValueError: raise ValueError("The sample rate must be a non-zero whole number. Please provide a valid integer.") - + self.use_soundfile = use_soundfile self.use_autocast = use_autocast @@ -582,85 +582,19 @@ def load_model_data_using_hash(self, model_path): self.logger.debug(f"MDX model data path set to {mdx_model_data_path}") self.download_file_if_not_exists(mdx_model_data_url, mdx_model_data_path) - # Loading model data + # Loading model data from UVR self.logger.debug("Loading MDX and VR model parameters from UVR model data files...") vr_model_data_object = json.load(open(vr_model_data_path, encoding="utf-8")) mdx_model_data_object = json.load(open(mdx_model_data_path, encoding="utf-8")) - # vr_model_data_object JSON structure / example snippet: - # { - # "0d0e6d143046b0eecc41a22e60224582": { - # "vr_model_param": "3band_44100_mid", - # "primary_stem": "Instrumental" - # }, - # "6b5916069a49be3fe29d4397ecfd73fa": { - # "vr_model_param": "3band_44100_msb2", - # "primary_stem": "Instrumental", - # "is_karaoke": true - # }, - # "0ec76fd9e65f81d8b4fbd13af4826ed8": { - # "vr_model_param": "4band_v3", - # "primary_stem": "No Woodwinds" - # }, - # "0fb9249ffe4ffc38d7b16243f394c0ff": { - # "vr_model_param": "4band_v3", - # "primary_stem": "No Reverb" - # }, - # "6857b2972e1754913aad0c9a1678c753": { - # "vr_model_param": "4band_v3", - # "primary_stem": "No Echo", - # "nout": 48, - # "nout_lstm": 128 - # }, - # "944950a9c5963a5eb70b445d67b7068a": { - # "vr_model_param": "4band_v3_sn", - # "primary_stem": "Vocals", - # "nout": 64, - # "nout_lstm": 128, - # "is_karaoke": false, - # "is_bv_model": true, - # "is_bv_model_rebalanced": 0.9 - # } - # } + # Load additional model data from audio-separator + self.logger.debug("Loading additional model parameters from audio-separator model data file...") + with resources.open_text("audio_separator", "model-data.json") as f: + audio_separator_model_data = json.load(f) - # mdx_model_data_object JSON structure / example snippet: - # { - # "0ddfc0eb5792638ad5dc27850236c246": { - # "compensate": 1.035, - # "mdx_dim_f_set": 2048, - # "mdx_dim_t_set": 8, - # "mdx_n_fft_scale_set": 6144, - # "primary_stem": "Vocals" - # }, - # "26d308f91f3423a67dc69a6d12a8793d": { - # "compensate": 1.035, - # "mdx_dim_f_set": 2048, - # "mdx_dim_t_set": 9, - # "mdx_n_fft_scale_set": 8192, - # "primary_stem": "Other" - # }, - # "2cdd429caac38f0194b133884160f2c6": { - # "compensate": 1.045, - # "mdx_dim_f_set": 3072, - # "mdx_dim_t_set": 8, - # "mdx_n_fft_scale_set": 7680, - # "primary_stem": "Instrumental" - # }, - # "2f5501189a2f6db6349916fabe8c90de": { - # "compensate": 1.035, - # "mdx_dim_f_set": 2048, - # "mdx_dim_t_set": 8, - # "mdx_n_fft_scale_set": 6144, - # "primary_stem": "Vocals", - # "is_karaoke": true - # }, - # "2154254ee89b2945b97a7efed6e88820": { - # "config_yaml": "model_2_stem_061321.yaml" - # }, - # "116f6f9dabb907b53d847ed9f7a9475f": { - # "config_yaml": "model_2_stem_full_band_8k.yaml" - # } - # } + # Merge the model data objects, with audio-separator data taking precedence + vr_model_data_object = {**vr_model_data_object, **audio_separator_model_data.get("vr_model_data", {})} + mdx_model_data_object = {**mdx_model_data_object, **audio_separator_model_data.get("mdx_model_data", {})} if model_hash in mdx_model_data_object: model_data = mdx_model_data_object[model_hash] @@ -669,7 +603,7 @@ def load_model_data_using_hash(self, model_path): else: raise ValueError(f"Unsupported Model File: parameters for MD5 hash {model_hash} could not be found in UVR model data file for MDX or VR arch.") - self.logger.debug(f"Model data loaded from UVR JSON using hash {model_hash}: {model_data}") + self.logger.debug(f"Model data loaded using hash {model_hash}: {model_data}") return model_data @@ -713,7 +647,7 @@ def load_model(self, model_filename="model_mel_band_roformer_ep_3005_sdr_11.4360 "output_single_stem": self.output_single_stem, "invert_using_spec": self.invert_using_spec, "sample_rate": self.sample_rate, - "use_soundfile": self.use_soundfile + "use_soundfile": self.use_soundfile, } # Instantiate the appropriate separator class depending on the model type diff --git a/pyproject.toml b/pyproject.toml index 576818a..fbeef66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "audio-separator" -version = "0.27.0" +version = "0.27.1" description = "Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07" authors = ["Andrew Beveridge "] license = "MIT" diff --git a/tests/model-metrics/test-all-models.py b/tests/model-metrics/test-all-models.py new file mode 100644 index 0000000..1a9bd81 --- /dev/null +++ b/tests/model-metrics/test-all-models.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python +import os +import museval +import numpy as np +import soundfile as sf +from audio_separator.separator import Separator +from pathlib import Path +import json +import logging +import musdb +from decimal import Decimal +import pandas as pd + + +# Setup logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +MUSDB_PATH = "tests/model-metrics/datasets/musdb18hq" +RESULTS_PATH = "tests/model-metrics/results" + + +def evaluate_track(track_name, track_path, test_model, mus_db): + """Evaluate a single track using a specific model""" + logger.info(f"Evaluating track: {track_name} with model: {test_model}") + + # Set output directory for this separation + output_dir = os.path.join(RESULTS_PATH, test_model, track_name) + os.makedirs(output_dir, exist_ok=True) + + # Check if evaluation results already exist + results_file = os.path.join(output_dir, "museval-results.json") + if os.path.exists(results_file): + logger.info("Found existing evaluation results, loading from file...") + with open(results_file) as f: + json_data = json.load(f) + scores = museval.TrackStore(track_name) + scores.scores = json_data + else: + # Standard stem output names + stem_mapping = {"Vocals": "vocals", "Instrumental": "instrumental", "Drums": "drums", "Bass": "bass", "Other": "other"} + + # Perform separation if needed + if not os.path.exists(output_dir) or not os.listdir(output_dir): + logger.info("Performing separation...") + separator = Separator(output_dir=output_dir) + separator.load_model(model_filename=test_model) + separator.separate(os.path.join(track_path, "mixture.wav"), custom_output_names=stem_mapping) + + # Check which stems were actually created + available_stems = {} + for musdb_name in ["vocals", "instrumental", "drums", "bass", "other"]: + stem_path = os.path.join(output_dir, f"{musdb_name}.wav") + if os.path.exists(stem_path): + available_stems[musdb_name if musdb_name != "instrumental" else "accompaniment"] = stem_path + + if not available_stems: + logger.info(f"No evaluatable stems found for model {test_model}, skipping evaluation") + return None, None + + # Get track from MUSDB + track = next((t for t in mus_db if t.name == track_name), None) + if track is None: + raise ValueError(f"Track {track_name} not found in MUSDB18") + + # Load available stems + estimates = {} + for stem_name, stem_path in available_stems.items(): + audio, _ = sf.read(stem_path) + if len(audio.shape) == 1: + audio = np.expand_dims(audio, axis=1) + estimates[stem_name] = audio + + # Evaluate using museval + logger.info(f"Evaluating stems: {list(estimates.keys())}") + scores = museval.eval_mus_track(track, estimates, output_dir=output_dir, mode="v4") + + # Move and rename the results file + test_results = os.path.join(output_dir, "test", f"{track_name}.json") + if os.path.exists(test_results): + os.rename(test_results, results_file) + os.rmdir(os.path.join(output_dir, "test")) + + # Calculate aggregate scores for available stems + results_store = museval.EvalStore() + results_store.add_track(scores.df) + methods = museval.MethodStore() + methods.add_evalstore(results_store, name=test_model) + agg_scores = methods.agg_frames_tracks_scores() + + # Return the aggregate scores in a structured format with 6 significant figures + model_results = {"track_name": track_name, "scores": {}} + + for stem in ["vocals", "drums", "bass", "other", "accompaniment"]: + try: + stem_scores = {metric: float(f"{agg_scores.loc[(test_model, stem, metric)]:.6g}") for metric in ["SDR", "SIR", "SAR", "ISR"]} + # Rename 'accompaniment' to 'instrumental' in the output + output_stem = "instrumental" if stem == "accompaniment" else stem + model_results["scores"][output_stem] = stem_scores + except KeyError: + continue + + return scores, model_results if model_results["scores"] else None + + +def convert_decimal_to_float(obj): + """Recursively converts Decimal objects to floats in a nested structure.""" + if isinstance(obj, Decimal): + return float(obj) + elif isinstance(obj, dict): + return {k: convert_decimal_to_float(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_decimal_to_float(x) for x in obj] + return obj + + +def calculate_median_scores(track_scores): + """Calculate median scores across all tracks for each stem and metric""" + # Initialize containers for each stem's metrics + stem_metrics = { + "vocals": {"SDR": [], "SIR": [], "SAR": [], "ISR": []}, + "drums": {"SDR": [], "SIR": [], "SAR": [], "ISR": []}, + "bass": {"SDR": [], "SIR": [], "SAR": [], "ISR": []}, + "instrumental": {"SDR": [], "SIR": [], "SAR": [], "ISR": []}, + } + + # Collect all scores for each stem and metric + for track_score in track_scores: + if track_score is not None and "scores" in track_score: + for stem, metrics in track_score["scores"].items(): + if stem in stem_metrics: + for metric, value in metrics.items(): + stem_metrics[stem][metric].append(value) + + # Calculate medians for each stem and metric + median_scores = {} + for stem, metrics in stem_metrics.items(): + if any(metrics.values()): # Only include stems that have scores + median_scores[stem] = {metric: float(f"{np.median(values):.6g}") for metric, values in metrics.items() if values} # Only include metrics that have values + + return median_scores if median_scores else None + + +def main(): + logger.info("Starting model evaluation script...") + os.makedirs(RESULTS_PATH, exist_ok=True) + + # Initialize MUSDB once at the start + logger.info("Initializing MUSDB database...") + mus = musdb.DB(root=MUSDB_PATH, is_wav=True) + + # Get list of all available models + logger.info("Getting list of available models...") + separator = Separator() + models_by_type = separator.list_supported_model_files() + + # Load existing results if available + combined_results_path = "audio_separator/models-scores.json" + combined_results = {} + if os.path.exists(combined_results_path): + logger.info("Loading existing combined results...") + with open(combined_results_path) as f: + combined_results = json.load(f) + + # Process all tracks in MUSDB18 + for track in mus.tracks: + track_name = track.name + track_path = os.path.dirname(track.path) + logger.info(f"Processing track: {track_name}") + + # Process all models for this track + for model_type, models in models_by_type.items(): + for model_name, model_info in models.items(): + test_model = None + if isinstance(model_info, str): + test_model = model_info + elif isinstance(model_info, dict): + for file_name in model_info.keys(): + if file_name.endswith((".onnx", ".pth", ".ckpt")): + test_model = file_name + break + + if test_model: + # Initialize model entry if it doesn't exist + if test_model not in combined_results: + combined_results[test_model] = {"model_name": model_name, "track_scores": [], "median_scores": None} + + # Check if track already evaluated + track_already_evaluated = any( + track_score["track_name"] == track_name for track_score in combined_results[test_model]["track_scores"] if track_score is not None # Handle null entries + ) + + if track_already_evaluated: + logger.info(f"Skipping already evaluated track {track_name} for model: {test_model}") + else: + logger.info(f"Processing model: {test_model}") + try: + _, model_results = evaluate_track(track_name, track_path, test_model, mus) + if model_results: + combined_results[test_model]["track_scores"].append(model_results) + else: + combined_results[test_model]["track_scores"].append(None) + except Exception as e: + logger.error(f"Error evaluating model {test_model} with track {track_name}: {str(e)}") + combined_results[test_model]["track_scores"].append(None) + continue + + # Calculate and update median scores regardless of whether track was evaluated + median_scores = calculate_median_scores(combined_results[test_model]["track_scores"]) + combined_results[test_model]["median_scores"] = median_scores + + # Save results after each model (whether evaluated or skipped) + os.makedirs(os.path.dirname(combined_results_path), exist_ok=True) + with open(combined_results_path, "w") as f: + json.dump(combined_results, f, indent=2) + + if not track_already_evaluated: + logger.info(f"Updated combined results file with {test_model} - {track_name}") + + logger.info("Evaluation complete") + return 0 + + +if __name__ == "__main__": + exit(main())