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"elif \"v3.5_raw\" in data:\n", " codes = data[\"v3.5_raw\"]\n", "elif \"v4.0_raw\" in data:\n", " codes = data[\"v4.0_raw\"]\n", "elif \"v5.0_raw\" in data:\n", " codes = data[\"v5.0_raw\"]\n", "else:\n", " raise ValueError(\"No codes found\")\n", "\n", "semantic_codes = torch.from_numpy(codes[:, 0]).long() # .cuda()\n", "# semantic_codes = semantic_codes[:3000]\n", "print(semantic_codes.shape)\n", "\n", "mp3_filepath = f\"s3://suno-data-uploads/studio/uploads/{item_id}.mp3\"\n", "\n", "# load the vae\n", "vae_data_filepath = f\"s3://suno-data-uploads/studio/uploads/{item_id}_vae.npz\"\n", "vae_data = read_from_s3(vae_data_filepath, read_f=np.load)\n", "print(vae_data.keys())\n", "init_vae_latents = vae_data[\"vae_latents\"]\n", "print(init_vae_latents.shape)\n", "\n", "gen_seed = vae_data.get(\"seed\", None)\n", "print(gen_seed)\n", "#tags_str = item[\"tags\"]\n", "\n", "#if tags_str is None:\n", "# tags_str = \"\"\n", "#lyrics = item[\"text\"]\n", "\n", "os.system(f\"aws s3 cp s3://suno-data-uploads/studio/uploads/{item_id}_hoot.json text_data.json\")\n", "text_data = open(\"text_data.json\", \"r\", encoding=\"utf-8\").read()\n", "aligned_lyrics = json.loads(text_data)\n", "print(aligned_lyrics)\n", "\n", "tags = \"\"\n", "\n", "lyrics = \"\"\n", "for elem in aligned_lyrics:\n", " if \"word\" in elem:\n", " lyrics += elem[\"word\"]" ] }, { "cell_type": "code", "execution_count": 20, "id": "3c459c16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'word': '[Intro]\\nI', 'start_s': 0.2393617021276596, 'end_s': 0.4787234042553192, 'success': True, 'p_align': 1.0}, {'word': ' dem', 'start_s': 0.4787234042553192, 'end_s': 0.7180851063829787, 'success': True, 'p_align': 1.0}, {'word': 'and', 'start_s': 0.7180851063829787, 'end_s': 0.9574468085106383, 'success': True, 'p_align': 1.0}, {'word': ' my', 'start_s': 1.1170212765957448, 'end_s': 1.3563829787234043, 'success': True, 'p_align': 1.0}, {'word': ' respect', 'start_s': 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Did I stammer?\n", "Did I stumble? Did I falter?\n", "Did I whisper? Did I mumble?\n", "Did I? Did I\n" ] } ], "source": [ "import random\n", "\n", "hoot_filepath = f\"s3://suno-data-uploads/studio/uploads/{item_id}_hoot.json\"\n", "text_data = read_from_s3(hoot_filepath, read_f=open)\n", "aligned_lyrics = json.loads(text_data.read())\n", "print(aligned_lyrics)\n", "\n", "# now i want to find the lyrics just from the 30s segment of the audio, \n", "# so for this we need a random start point in seconds, lets try\n", "random_start_point = 120\n", "print(random_start_point)\n", "\n", "# now find the lyrics for the 30s segment\n", "lyrics_segment = \"\"\n", "for elem in aligned_lyrics:\n", " if \"word\" in elem:\n", " start_s = elem[\"start_s\"]\n", " end_s = elem[\"end_s\"]\n", " if start_s >= random_start_point and end_s <= random_start_point + 30:\n", " lyrics_segment += elem[\"word\"]\n", "print(lyrics_segment)\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2bd4425c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "download: s3://suno-data-uploads/studio/uploads/ebbddbbc-f0fa-400e-9789-94978fb0bf05_hoot.json to ./text_data.json\n", "[{'word': '[Intro]\\nI', 'start_s': 0.2393617021276596, 'end_s': 0.4787234042553192, 'success': True, 'p_align': 1.0}, {'word': ' dem', 'start_s': 0.4787234042553192, 'end_s': 0.7180851063829787, 'success': True, 'p_align': 1.0}, {'word': 'and', 'start_s': 0.7180851063829787, 'end_s': 0.9574468085106383, 'success': True, 'p_align': 1.0}, {'word': ' my', 'start_s': 1.1170212765957448, 'end_s': 1.3563829787234043, 'success': True, 'p_align': 1.0}, {'word': ' respect', 'start_s': 1.675531914893617, 'end_s': 2.154255319148936, 'success': True, 'p_align': 1.0}, {'word': '\\n\\n[Chorus]\\nDid', 'start_s': 2.154255319148936, 'end_s': 2.393617021276596, 'success': True, 'p_align': 1.0}, {'word': ' I', 'start_s': 2.393617021276596, 'end_s': 2.5531914893617023, 'success': True, 'p_align': 1.0}, {'word': ' st', 'start_s': 2.7127659574468086, 'end_s': 2.7925531914893615, 'success': True, 'p_align': 1.0}, {'word': 'ut', 'start_s': 2.7925531914893615, 'end_s': 2.9521276595744683, 'success': True, 'p_align': 1.0}, {'word': 'ter', 'start_s': 2.9521276595744683, 'end_s': 3.1914893617021276, 'success': True, 'p_align': 1.0}, {'word': '? 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Did', 'start_s': 160.45212765957447, 'end_s': 160.85106382978722, 'success': True, 'p_align': 1.0}, {'word': ' I?\\n\\n', 'start_s': 168.2712765957447, 'end_s': 168.67021276595744, 'success': True, 'p_align': 1.0}, {'hoot_lyrics': \"i demand my respect did i stutter did i stummer did i stumble did i falter did i whisper did i mumble did i did i did i stutter did i stummer did i stumble did i falter did i whisper did i mumble did i did i i demand my respect did i st up did i stumble did i stumble did i falter did i whisper did i mumble did i did i did i st up did i stumble did i stumble did i falter did i whisper did i mumble did i did i who you know get my bread than me keep your hoe in bed with me who gon' catch my speed if i say i got the lead got that dog up in my chest had it since my life begun give a damn 'bout the police it's on sight i'll kill one and don't play with my kids either boy i'm the one find out how many babies i left stuck to they mum you a fun sized run i'm the sun heard the sauce and the slum not a crumb had toand my respect i demand my respect did i stutter did i stumble did i stumble did i falter did i whisper did i mumble did i did i did i stutter did i stumble did i stumble did i falter did i whisper did i mumble did i did i did i stter did i stumble did i stumble did i falter did i whisper did i mum i up i stumble stumble i falter whisper i\"}, {'hoot_cer': 0.40888066604995377}, {'decoder': 'ShortestPathAligner'}, {'metadata': {'audio_rms': [0.02392, 0.0828, 0.07905, 0.10173, 0.09807, 0.07775, 0.10362, 0.08905, 0.07444, 0.05506, 0.01279, 0.10414, 0.09202, 0.245, 0.1608, 0.12706, 0.2839, 0.16977, 0.26358, 0.159, 0.08094, 0.28301, 0.13687, 0.26097, 0.18021, 0.16572, 0.27603, 0.10443, 0.27483, 0.15787, 0.03933, 0.2841, 0.15495, 0.27656, 0.16213, 0.12184, 0.26518, 0.15569, 0.27765, 0.15349, 0.07662, 0.28395, 0.10864, 0.25161, 0.16563, 0.11318, 0.28304, 0.15925, 0.26889, 0.14379, 0.03973, 0.277, 0.12031, 0.27149, 0.15839, 0.11863, 0.28655, 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0.23833, 0.16868, 0.06856, 0.30545, 0.04813, 0.23923, 0.16829, 0.06862, 0.306, 0.05834, 0.23481, 0.1624, 0.0652, 0.32293, 0.04789, 0.2401, 0.16573, 0.06966, 0.30638, 0.04355, 0.24528, 0.16557, 0.06458, 0.30865, 0.0406, 0.24903, 0.16426, 0.06731, 0.30814, 0.04247, 0.24697, 0.16034, 0.06251, 0.30495, 0.0383, 0.25031, 0.16051, 0.07415, 0.31631, 0.06431, 0.24586, 0.15167, 0.06283, 0.30478, 0.03753, 0.25425, 0.15626, 0.06253, 0.30378, 0.04387, 0.25544, 0.15106, 0.06248, 0.30514, 0.04034, 0.25786, 0.15111, 0.06437, 0.30657, 0.03347, 0.26163, 0.15053, 0.06517, 0.33092, 0.04206, 0.26511, 0.14863, 0.06202, 0.30645, 0.03976, 0.26388, 0.14395, 0.05916, 0.30918, 0.04218, 0.26775, 0.14672, 0.04396, 0.01316, 0.00542, 0.003, 0.00134, 0.00083, 0.00054]}}]\n" ] } ], "source": [ "# get lyrics from another song to test alignment\n", "\n", "item_id = \"ebbddbbc-f0fa-400e-9789-94978fb0bf05\"\n", "os.system(f\"aws s3 cp s3://suno-data-uploads/studio/uploads/{item_id}_hoot.json text_data.json\")\n", "\n", "\n", "\n", "text_data = open(\"text_data.json\", \"r\", encoding=\"utf-8\").read()\n", "aligned_lyrics = json.loads(text_data)\n", "print(aligned_lyrics)\n", "\n", "tags = \"\"\n", "\n", "lyrics = \"\"\n", "for elem in aligned_lyrics:\n", " if \"word\" in elem:\n", " lyrics += elem[\"word\"]" ] }, { "cell_type": "code", "execution_count": 9, "id": "f819ff2b", "metadata": {}, "outputs": [], "source": [ "def text_to_ids(text: str, labels) -> torch.Tensor:\n", " \"\"\"\n", " Normalize text to match the model's label set.\n", " Keeps only characters that appear in `labels`.\n", " Spaces are mapped to the model's space symbol (' ' or '|').\n", " \"\"\"\n", " label_to_id = {s: i for i, s in enumerate(labels)}\n", "\n", " # pick the model's space symbol\n", " space_symbol = \" \" if \" \" in label_to_id else \"|\"\n", "\n", " text = text.lower()\n", " ids = []\n", " for ch in text:\n", " if ch == \" \":\n", " ids.append(label_to_id[space_symbol])\n", " elif ch in label_to_id:\n", " ids.append(label_to_id[ch])\n", " # else: skip unsupported characters\n", "\n", " return torch.tensor([ids], dtype=torch.int32) # shape (1, L)" ] }, { "cell_type": "code", "execution_count": 13, "id": "bec4534a", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "targets Tensor shouldn't contain blank index. Found tensor([[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 24, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 24, 1, 1, 1, 24, 1, 1, 1, 24, 1, 1, 1, 24, 1, 1, 1, 1, 1,\n 1, 1, 24, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n 1, 1, 24, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int32).", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 33\u001b[39m\n\u001b[32m 30\u001b[39m log_probs = log_probs[\u001b[32m0\u001b[39m:\u001b[32m1\u001b[39m, :, :] \u001b[38;5;66;03m# keep shape (1, T, C)\u001b[39;00m\n\u001b[32m 32\u001b[39m \u001b[38;5;66;03m# 5. Forced alignment\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m33\u001b[39m alignment, scores = \u001b[43mtorchaudio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfunctional\u001b[49m\u001b[43m.\u001b[49m\u001b[43mforced_align\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 34\u001b[39m \u001b[43m \u001b[49m\u001b[43mlog_probs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# (B, T, C)\u001b[39;49;00m\n\u001b[32m 35\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_ids\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# (B, L)\u001b[39;49;00m\n\u001b[32m 36\u001b[39m \u001b[43m \u001b[49m\u001b[43minput_lengths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 37\u001b[39m \u001b[43m \u001b[49m\u001b[43mtarget_lengths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 38\u001b[39m \u001b[43m \u001b[49m\u001b[43mblank\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblank_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 39\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAlignment path (per frame token ids):\u001b[39m\u001b[33m\"\u001b[39m, alignment.shape)\n\u001b[32m 42\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mScores (log-prob per frame):\u001b[39m\u001b[33m\"\u001b[39m, scores.shape)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/suno_diff/lib/python3.12/site-packages/torchaudio/functional/_alignment.py:57\u001b[39m, in \u001b[36mforced_align\u001b[39m\u001b[34m(log_probs, targets, input_lengths, target_lengths, blank)\u001b[39m\n\u001b[32m 19\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33mr\u001b[39m\u001b[33;03m\"\"\"Align a CTC label sequence to an emission.\u001b[39;00m\n\u001b[32m 20\u001b[39m \n\u001b[32m 21\u001b[39m \u001b[33;03m.. devices:: CPU CUDA\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 54\u001b[39m \u001b[33;03m The current version only supports ``batch_size==1``.\u001b[39;00m\n\u001b[32m 55\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m blank \u001b[38;5;129;01min\u001b[39;00m targets:\n\u001b[32m---> \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mtargets Tensor shouldn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt contain blank index. Found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtargets\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch.max(targets) >= log_probs.shape[-\u001b[32m1\u001b[39m]:\n\u001b[32m 59\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mtargets values must be less than the CTC dimension\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[31mValueError\u001b[39m: targets Tensor shouldn't contain blank index. Found tensor([[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 24, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 24, 1, 1, 1, 24, 1, 1, 1, 24, 1, 1, 1, 24, 1, 1, 1, 1, 1,\n 1, 1, 24, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n 1, 1, 24, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int32)." ] } ], "source": [ "import torch\n", "import torchaudio\n", "\n", "# 1. Load model + labels\n", "bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H\n", "model = bundle.get_model().eval()\n", "labels = bundle.get_labels()\n", "blank_id = 0 # for this bundle\n", "\n", "# 2. Load audio\n", "waveform, sr = read_from_s3(mp3_filepath, read_f=torchaudio.load)\n", "\n", "if sr != bundle.sample_rate:\n", " waveform = torchaudio.functional.resample(waveform, sr, bundle.sample_rate)\n", "\n", "# 3. Get emission log-probs (B, T, C)\n", "with torch.no_grad():\n", " emissions, _ = model(waveform) # [1, T, C] logits\n", "log_probs = torch.log_softmax(emissions, dim=-1)\n", "\n", "# 4. Prepare target sequence (text → label ids)\n", "# For this bundle, space is represented by '|'\n", "target_ids = text_to_ids(lyrics, labels)\n", "\n", "input_lengths = torch.tensor([log_probs.shape[1]], dtype=torch.int32)\n", "target_lengths = torch.tensor([target_ids.shape[1]], dtype=torch.int32)\n", "\n", "if log_probs.shape[0] != 1:\n", " # pick the first utterance if you somehow passed multiple\n", " log_probs = log_probs[0:1, :, :] # keep shape (1, T, C)\n", "\n", "# 5. Forced alignment\n", "alignment, scores = torchaudio.functional.forced_align(\n", " log_probs, # (B, T, C)\n", " target_ids, # (B, L)\n", " input_lengths,\n", " target_lengths,\n", " blank=blank_id,\n", ")\n", "\n", "print(\"Alignment path (per frame token ids):\", alignment.shape)\n", "print(\"Scores (log-prob per frame):\", scores.shape)\n", "print(\"Mean log-prob over path:\", scores.mean().item())\n" ] }, { "cell_type": "code", "execution_count": null, "id": "debd7dd1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "suno_diff", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 }