{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "fed7c348", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "id": "daf4a2da", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"5\"\n", "import boto3\n", "\n", "# Clear any cached default session (important if you've used boto3 before in this session)\n", "boto3.DEFAULT_SESSION = None\n", "\n", "# Unset any env variable overriding the profile\n", "os.environ.pop(\"AWS_PROFILE\", None)\n", "os.environ.pop(\"AWS_DEFAULT_PROFILE\", None)" ] }, { "cell_type": "code", "execution_count": 7, "id": "c046f7b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Failed to import flash_attn_3.\n", "Running on non-Modal\n", "Dir1: 182 files\n", "Dir2: 3 files\n", "Common files: 3\n", "All unique files: 182\n", "Match percentage: 1.65%\n", "Similarity: 1.65%\n", "Directories are not identical. Use disk model instead, if you want to use ssd models. run python -m suno_utils.worker.modal_manager_model_store sync\n", "Failed to import flash_attn_3.\n", "No module named 'flash_attn_interface'\n", "WILL USE FLASH ATTN: True\n", "flashinfer not installed, using torch sampling\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zoe/glockenspiel/suno_utils/suno_utils/gpt/engine.py:221: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", " @autocast()\n" ] }, { "ename": "ClientError", "evalue": "An error occurred (400) when calling the HeadObject operation: Bad Request", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[7], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m engine \u001b[38;5;241m=\u001b[39m UpsampleEngine(min_chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m25\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m30\u001b[39m, vae_version\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mv_vae_25_tuned_2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01msuno_utils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtasks\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maudio_features\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbeat_this_downbeat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BeatThisDownbeatExtractor\n\u001b[0;32m---> 20\u001b[0m downbeat_extractor \u001b[38;5;241m=\u001b[39m \u001b[43mBeatThisDownbeatExtractor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcuda\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43ms3://suno-data/m4burns/beat_this_beta.pt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 22\u001b[0m \u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/glockenspiel/suno_utils/suno_utils/tasks/audio_features/beat_this_downbeat.py:70\u001b[0m, in \u001b[0;36mBeatThisDownbeatExtractor.__init__\u001b[0;34m(self, model_path, device, beat_diff_median_ratio)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m onset_times, onset_values\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_onsets \u001b[38;5;241m=\u001b[39m _get_onsets\n\u001b[0;32m---> 70\u001b[0m local_ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[43m_get_model_if_needed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m Audio2Beats(local_ckpt_path, device\u001b[38;5;241m=\u001b[39mdevice, dbn\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbeat_diff_median_ratio \u001b[38;5;241m=\u001b[39m beat_diff_median_ratio\n", "File \u001b[0;32m~/glockenspiel/suno_utils/suno_utils/gpt/chirp_v2_5.py:113\u001b[0m, in \u001b[0;36m_get_model_if_needed\u001b[0;34m(model_path, cache_dir)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_path\n\u001b[1;32m 112\u001b[0m \u001b[38;5;66;03m# remote model, fetch if needed\u001b[39;00m\n\u001b[0;32m--> 113\u001b[0m checksum \u001b[38;5;241m=\u001b[39m \u001b[43mget_s3_checksum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;66;03m# get temp path\u001b[39;00m\n\u001b[1;32m 116\u001b[0m local_tmp \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/mnt/localdisk/cached_ckpts\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/mnt/localdisk\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[0;32m~/glockenspiel/suno_utils/suno_utils/utils/s3.py:147\u001b[0m, in \u001b[0;36mget_s3_checksum\u001b[0;34m(filepath, client_config)\u001b[0m\n\u001b[1;32m 145\u001b[0m bucket_name, rel_s3_filepath \u001b[38;5;241m=\u001b[39m _parse_s3_filepath(filepath)\n\u001b[1;32m 146\u001b[0m client \u001b[38;5;241m=\u001b[39m _get_client(client_config\u001b[38;5;241m=\u001b[39mclient_config, sample_s3_filepath\u001b[38;5;241m=\u001b[39mfilepath)\n\u001b[0;32m--> 147\u001b[0m s3_resp \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBucket\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbucket_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mKey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_s3_filepath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m s3_resp[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mETag\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mstrip(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/botocore/client.py:595\u001b[0m, in \u001b[0;36mClientCreator._create_api_method.._api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 592\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpy_operation_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m() only accepts keyword arguments.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 593\u001b[0m )\n\u001b[1;32m 594\u001b[0m \u001b[38;5;66;03m# The \"self\" in this scope is referring to the BaseClient.\u001b[39;00m\n\u001b[0;32m--> 595\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_api_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43moperation_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/botocore/context.py:123\u001b[0m, in \u001b[0;36mwith_current_context..decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hook:\n\u001b[1;32m 122\u001b[0m hook()\n\u001b[0;32m--> 123\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/botocore/client.py:1058\u001b[0m, in \u001b[0;36mBaseClient._make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m 1054\u001b[0m error_code \u001b[38;5;241m=\u001b[39m error_info\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQueryErrorCode\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m error_info\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1055\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCode\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1056\u001b[0m )\n\u001b[1;32m 1057\u001b[0m error_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mfrom_code(error_code)\n\u001b[0;32m-> 1058\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_class(parsed_response, operation_name)\n\u001b[1;32m 1059\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1060\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parsed_response\n", "\u001b[0;31mClientError\u001b[0m: An error occurred (400) when calling the HeadObject operation: Bad Request" ] } ], "source": [ "from suno_utils.diffusion import generation as diffusion_gen\n", "from suno_utils.tasks.upsample_engine_old import UpsampleEngine, Request, Job\n", "from suno_utils.tasks.dac_vae_fixed_25hz import (\n", " decode_stream_to_full_audio,\n", " encode_files,\n", ")\n", "import torch\n", "from suno_utils.audio import Audio\n", "from suno_utils.gpt.generation import GenerationConfig, CfgGenerationConfig\n", "from suno_utils.gpt.engine import Engine\n", "from suno_utils.gpt.generation_engine import make_request\n", "from suno_utils.utils.clip import SunoClip\n", "from suno_utils.gpt.generation_prompt import ALL_AUDIO_PROMPTS\n", "\n", "import sys\n", "\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"5\"\n", "sys.path.insert(0, \"neon/sunoDiff/\")\n", "import numpy as np\n", "from suno_utils.utils.s3 import read_from_s3\n", "\n", "engine = UpsampleEngine(min_chunk_size=25 * 30, vae_version=\"v_vae_25_tuned_2\")\n", "from suno_utils.tasks.audio_features.beat_this_downbeat import BeatThisDownbeatExtractor\n", "\n", "downbeat_extractor = BeatThisDownbeatExtractor(\n", " device=\"cuda\", model_path=\"s3://suno-data/m4burns/beat_this_beta.pt\"\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "a718fb2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading semantic model...\n", "cropped extra layers of MERT model to 7\n", "loading v2 codec model...\n", "loading diffusion model...\n", "loading weights...\n", "converting model to precision torch.bfloat16...\n", "dit_model loaded onto cuda:0\n", "loading tokenizer...\n", "done!\n" ] }, { "data": { "text/plain": [ "(DiffusionTransformer(\n", " (semantic_conditioner): SemanticConditioner(\n", " (embedding): Embedding(4001, 768)\n", " (proj_out): Linear(in_features=768, out_features=2048, bias=True)\n", " )\n", " (text_conditioner): TextConditioner(\n", " (embedding): Embedding(60001, 2048)\n", " )\n", " (ctx_conditioner): LatentContextConditioner(\n", " (proj_out): Linear(in_features=128, out_features=2048, bias=True)\n", " )\n", " (infill_ctx_conditioner): LatentContextConditioner(\n", " (proj_out): Linear(in_features=128, out_features=2048, bias=True)\n", " )\n", " (timestep_features): FourierFeatures()\n", " (to_timestep_embed): Sequential(\n", " (0): Linear(in_features=256, out_features=2048, bias=True)\n", " (1): SiLU()\n", " (2): Linear(in_features=2048, out_features=2048, bias=True)\n", " )\n", " (transformer): ContinuousTransformer(\n", " (layers): ModuleList(\n", " (0-31): 32 x TransformerBlock(\n", " (pre_norm): LayerNorm()\n", " (self_attn): Attention(\n", " (to_qkv): Linear(in_features=2048, out_features=6144, bias=False)\n", " (q_ln): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (k_ln): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (to_out): Linear(in_features=2048, out_features=2048, bias=False)\n", " )\n", " (ff_norm): LayerNorm()\n", " (ff): FeedForward(\n", " (ff): Sequential(\n", " (0): GLU(\n", " (act): SiLU()\n", " (proj): Linear(in_features=2048, out_features=16384, bias=True)\n", " )\n", " (1): Linear(in_features=8192, out_features=2048, bias=True)\n", " )\n", " )\n", " )\n", " )\n", " (project_in): Linear(in_features=128, out_features=2048, bias=False)\n", " (project_out): Linear(in_features=2048, out_features=128, bias=False)\n", " (rotary_pos_emb): RotaryEmbedding()\n", " )\n", " (preprocess_conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)\n", " (postprocess_conv): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)\n", " ),\n", " {'io_hz': 25,\n", " 'io_channels': 128,\n", " 'embed_dim': 2048,\n", " 'depth': 32,\n", " 'n_heads': 32,\n", " 'qk_norm': True,\n", " 'block_size': 750,\n", " 'cond_semantic_n_vocab': 4001,\n", " 'cond_semantic_len': 750,\n", " 'cond_text_n_vocab': 60001,\n", " 'cond_text_len': 1536,\n", " 'ctx_len': 750,\n", " 'infill_ctx_len': 0,\n", " 'stem_ctx_len': 0,\n", " 'shared_ctx': True})" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diffusion_gen.preload_models(\n", " # dit_model_filepath=\"/app/suno/victor/2b_prefix_ft.pt\",\n", " # codec_filepath=\"/app/suno/victor/25hz_vae_peaq_kl_0.005.pth\",\n", " # dit_model_filepath=\"/app/suno/checkpoints/2025-03-24_22-31-58_s4919/last_ckpt_infer.pt\", # finetune infill fixed\n", " # dit_model_filepath=\"/app/suno/checkpoints/2025-03-28_18-46-17_s894/last_ckpt_infer.pt\", # base model\n", " dit_model_filepath=\"/app/suno/modal/models/tony/tmp/diff/v45_2b_step_2mil_ft_8k_infill_apr21_t1_18_tt0.pt\", # base model\n", " codec_filepath=\"s3://suno-data/minz/models/dac_vae_tuned_25hz.pth\",\n", ")" ] }, { "cell_type": "markdown", "id": "72cbbdd6", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 4, "id": "9ddf0c9f", "metadata": {}, "outputs": [], "source": [ "history_len_s = 10\n", "future_len_s = 10\n", "dur_s = 10\n", "before_file = \"c7a77f50-9bcb-43d7-b0a3-acf9bbc25907\"\n", "after_file = \"109c8be1-82b8-43bd-9ba3-e93e14c35a40\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "6aa34552-850b-45b2-b301-b1db64adf779", "metadata": {}, "outputs": [], "source": [ "def get_all_encodings(s3_id):\n", " s3_filepath = f\"s3://suno-data-uploads/studio/uploads/{s3_id}.npz\"\n", " mp3_filepath = f\"s3://suno-data-uploads/studio/uploads/{s3_id}.mp3\"\n", " vae_filepath = f\"s3://suno-data-uploads/studio/uploads/{s3_id}_vae.npz\"\n", " vae_data = read_from_s3(vae_filepath, read_f=np.load)\n", " s3_data = read_from_s3(s3_filepath, read_f=np.load)\n", " if vae_data.get(\"vae_latents\", None) is None:\n", " print(s3_data)\n", " # Try v5.0_raw, fallback to v4.5_raw if not present\n", " sem_codes = None\n", " if \"v5.0_raw\" in s3_data:\n", " sem_codes = s3_data[\"v5.0_raw\"]\n", " elif \"v3.0_raw\" in s3_data:\n", " sem_codes = s3_data[\"v3.0_raw\"]\n", " elif \"v4.0_raw\" in s3_data:\n", " sem_codes = s3_data[\"v4.0_raw\"]\n", " print(sem_codes.shape)\n", " return {\n", " \"sem_codes\": sem_codes,\n", " \"vae_data\": vae_data[\"vae_latents\"],\n", " \"audio\": Audio.from_s3(mp3_filepath, n_channels=2),\n", " }" ] }, { "cell_type": "code", "execution_count": 6, "id": "6222e6a9", "metadata": {}, "outputs": [], "source": [ "def merge_songs(before_file, after_file, history_len_s=10, future_len_s=5, dur_s=10):\n", " assert history_len_s + future_len_s + dur_s == 30\n", " before_song = get_all_encodings(before_file)\n", " after_song = get_all_encodings(after_file)\n", " with contextlib.redirect_stdout(io.StringIO()):\n", " beat_times_before = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(before_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", " beat_times_after = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(after_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", "\n", " continue_at = beat_times_before.iloc[-1] - 5\n", " pickup_at = 5\n", " pickup_at = beat_times_after[beat_times_after > pickup_at + 0.2].iloc[0] - 0.2\n", " continue_at = beat_times_before[beat_times_before > continue_at].iloc[0] - 0.2\n", "\n", " continue_at = continue_at * 25\n", " pickup_at = pickup_at * 25\n", " hist_start = int(continue_at - history_len_s * 25 - (dur_s / 2) * 25)\n", " hist_end = hist_start + history_len_s * 25\n", " dur = 25 * dur_s\n", " fut_start = int(pickup_at)\n", " fut_end = fut_start + future_len_s * 25\n", "\n", " sem_codes = np.concatenate(\n", " [\n", " before_song[\"sem_codes\"][hist_start : hist_end + int(dur / 2), :],\n", " after_song[\"sem_codes\"][fut_start - int(dur / 2) : fut_end],\n", " ],\n", " axis=0,\n", " )\n", "\n", " padding = np.ones((int(dur / 2), 1)) * 4000\n", " # padding for first half of infill region\n", " sem_codes[hist_end - hist_start : hist_end - hist_start + int(dur / 2), :][::4] = (\n", " padding[::4]\n", " )\n", "\n", " # skip semantic for second half of infill region\n", " sem_codes[hist_end - hist_start + int(dur / 2) : hist_end - hist_start + dur, :][\n", " ::3\n", " ] = padding[::3]\n", "\n", " infill_prefix_latents = torch.from_numpy(\n", " before_song[\"vae_data\"][hist_start:hist_end, :]\n", " )\n", " infill_suffix_latents = torch.from_numpy(\n", " after_song[\"vae_data\"][fut_start:fut_end, :] # 5 sec after gen\n", " )\n", "\n", " gen_cfg = diffusion_gen.DiffusionGenerationConfig(\n", " audio=None,\n", " steps=32,\n", " lyrics=\"instrumental\",\n", " tags=\"\",\n", " text_cfg_coef=4.0,\n", " codec_scale_factor=0.4,\n", " scale_ctx_vector=True,\n", " infill_prefix_latents=infill_prefix_latents,\n", " infill_suffix_latents=infill_suffix_latents,\n", " noise_ctx_level=0.2,\n", " drop_semantic_tokens=True,\n", " )\n", "\n", " request = Request(\n", " id=\"dummy\",\n", " generation_config=gen_cfg,\n", " tokens=sem_codes,\n", " input_tokens_finished=True,\n", " )\n", " result = engine.run_request(request).vae_latents\n", " aud = decode_stream_to_full_audio(torch.concat(result))\n", " output[before_file].append(aud)" ] }, { "cell_type": "code", "execution_count": 7, "id": "bb705cd4", "metadata": {}, "outputs": [], "source": [ "def merge(before_file, after_file):\n", " min_chunk_s = 30\n", "\n", " dur_s, history_len_s, future_len_s = 10, 10, 10\n", " assert history_len_s + future_len_s + dur_s == min_chunk_s\n", "\n", " before_song = get_all_encodings(before_file)\n", " after_song = get_all_encodings(after_file)\n", " continue_at = before_song[\"audio\"].duration_s - 5\n", " pickup_at = 5\n", "\n", " import pandas as pd\n", "\n", " with contextlib.redirect_stdout(io.StringIO()):\n", " beat_times_before = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(before_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", " beat_times_after = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(after_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", " continue_at = beat_times_before[beat_times_before > continue_at].iloc[0] + 0.2\n", " pickup_at = beat_times_after[beat_times_after > pickup_at + 0.2].iloc[0] - 0.2\n", "\n", " continue_at = continue_at * 25\n", " pickup_at = pickup_at * 25\n", " hist_start = int(continue_at - history_len_s * 25 - (dur_s / 2) * 25)\n", " hist_end = hist_start + history_len_s * 25\n", " dur = 25 * dur_s\n", " fut_start = int(pickup_at)\n", " fut_end = fut_start + future_len_s * 25\n", "\n", " sem_codes = np.concatenate(\n", " [\n", " before_song[\"sem_codes\"][hist_start : hist_end + int(dur / 2), :],\n", " after_song[\"sem_codes\"][fut_start - int(dur / 2) : fut_end],\n", " ],\n", " axis=0,\n", " )\n", "\n", " padding = np.ones((int(dur / 2), 1)) * 4000\n", " # padding for first half of infill region\n", " sem_codes[hist_end - hist_start : hist_end - hist_start + int(dur / 2), :][::4] = (\n", " padding[::4]\n", " )\n", "\n", " # skip semantic for second half of infill region\n", " sem_codes[hist_end - hist_start + int(dur / 2) : hist_end - hist_start + dur, :][\n", " ::3\n", " ] = padding[::3]\n", "\n", " infill_prefix_latents = torch.from_numpy(\n", " before_song[\"vae_data\"][hist_start:hist_end, :]\n", " )\n", " infill_suffix_latents = torch.from_numpy(\n", " after_song[\"vae_data\"][fut_start:fut_end, :] # 5 sec after gen\n", " )\n", "\n", " gen_cfg = diffusion_gen.DiffusionGenerationConfig(\n", " audio=None,\n", " steps=32,\n", " lyrics=\"instrumental\",\n", " tags=\"\",\n", " text_cfg_coef=4.0,\n", " codec_scale_factor=0.4,\n", " scale_ctx_vector=True,\n", " infill_prefix_latents=infill_prefix_latents,\n", " infill_suffix_latents=infill_suffix_latents,\n", " noise_ctx_level=0.2,\n", " drop_semantic_tokens=True,\n", " )\n", " request = Request(\n", " id=\"dummy\",\n", " generation_config=gen_cfg,\n", " tokens=sem_codes,\n", " input_tokens_finished=True,\n", " )\n", " new_vae = torch.from_numpy(\n", " np.concatenate([before_song[\"vae_data\"], after_song[\"vae_data\"]], axis=0)\n", " )\n", " result = engine.run_request(request).vae_latents\n", " return decode_stream_to_full_audio(torch.concat(result))" ] }, { "cell_type": "code", "execution_count": 4, "id": "25ca1746", "metadata": {}, "outputs": [], "source": [ "song_pairs = [\n", " [\n", " \"b615c8d2-2285-4adf-a0ef-722dec2b65f1\",\n", " \"f97eb9ce-7eca-4487-bf7e-4ba9e580d62d\",\n", " ], # Jazz Fusion + Country\n", " [\n", " \"358da8a1-d831-4991-b476-7bf24fc9c297\",\n", " \"5c5c9447-99eb-439f-85d9-563e27758dfe\",\n", " ], # Blues + Hip-Hop Beat\n", " [\n", " \"68e8c8bb-6922-4d36-93ca-007a964e3f59\",\n", " \"50edf017-19bc-43bd-af30-7591ca9d9bdd\",\n", " ], # Reggae + Classical Piano\n", " [\n", " \"0ff2e4d4-e5db-4a54-8a41-909da0a9ad32\",\n", " \"98c261d4-b98c-44dd-9ea0-d09d7d07c5a7\",\n", " ], # Folk + Rock Ballad\n", " [\n", " \"c7a77f50-9bcb-43d7-b0a3-acf9bbc25907\",\n", " \"109c8be1-82b8-43bd-9ba3-e93e14c35a40\",\n", " ], # Ambient Electronic + Electronic\n", "]\n", "\n", "genre_to_id = {\n", " \"Jazz Fusion\": \"b615c8d2-2285-4adf-a0ef-722dec2b65f1\",\n", " \"Classical Piano\": \"50edf017-19bc-43bd-af30-7591ca9d9bdd\",\n", " \"Rock Ballad\": \"98c261d4-b98c-44dd-9ea0-d09d7d07c5a7\",\n", " \"Hip-Hop Beat\": \"5c5c9447-99eb-439f-85d9-563e27758dfe\",\n", " \"Reggae\": \"68e8c8bb-6922-4d36-93ca-007a964e3f59\",\n", " \"Country\": \"f97eb9ce-7eca-4487-bf7e-4ba9e580d62d\",\n", " \"Folk\": \"0ff2e4d4-e5db-4a54-8a41-909da0a9ad32\",\n", " \"Blues\": \"358da8a1-d831-4991-b476-7bf24fc9c297\",\n", " \"Electronic\": \"109c8be1-82b8-43bd-9ba3-e93e14c35a40\",\n", " \"Ambient Electronic\": \"c7a77f50-9bcb-43d7-b0a3-acf9bbc25907\",\n", "}" ] }, { "cell_type": "code", "execution_count": 9, "id": "6cecd40b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7598, 1)\n", "(5808, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.77s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.57s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.66s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(5624, 1)\n", "(3973, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.68s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.68s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(4100, 1)\n", "(4112, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.68s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(5300, 1)\n", "(4599, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.66s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.57s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(3254, 1)\n", "(6492, 1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.68s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.68s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.67s/it]\n", "Engine step: 0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "do infill...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Engine step: 1it [00:01, 1.57s/it]\n" ] } ], "source": [ "min_chunk_s = 30\n", "\n", "dur_s, history_len_s, future_len_s = 10, 10, 10\n", "assert history_len_s + future_len_s + dur_s == min_chunk_s\n", "output = {}\n", "\n", "for pair in song_pairs:\n", " before_file = pair[0]\n", " after_file = pair[1]\n", " output[before_file] = []\n", " before_song = get_all_encodings(before_file)\n", " after_song = get_all_encodings(after_file)\n", "\n", " import pandas as pd\n", "\n", " with contextlib.redirect_stdout(io.StringIO()):\n", " beat_times_before = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(before_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", " beat_times_after = pd.Series(\n", " [\n", " i[0]\n", " for i in (\n", " downbeat_extractor.extract(after_song[\"audio\"].mono())[\"downbeats\"]\n", " )\n", " ]\n", " )\n", "\n", " continue_at = beat_times_before.iloc[-1] - 5\n", " pickup_at = 5\n", " pickup_at = beat_times_after[beat_times_after > pickup_at + 0.2].iloc[0] - 0.2\n", " continue_at = beat_times_before[beat_times_before > continue_at].iloc[0] - 0.2\n", "\n", " continue_at = continue_at * 25\n", " pickup_at = pickup_at * 25\n", " hist_start = int(continue_at - history_len_s * 25 - (dur_s / 2) * 25)\n", " hist_end = hist_start + history_len_s * 25\n", " dur = 25 * dur_s\n", " fut_start = int(pickup_at)\n", " fut_end = fut_start + future_len_s * 25\n", "\n", " sem_codes = np.concatenate(\n", " [\n", " before_song[\"sem_codes\"][hist_start : hist_end + int(dur / 2), :],\n", " after_song[\"sem_codes\"][fut_start - int(dur / 2) : fut_end],\n", " ],\n", " axis=0,\n", " )\n", "\n", " padding = np.ones((int(dur / 2), 1)) * 4000\n", " # padding for first half of infill region\n", " sem_codes[hist_end - hist_start : hist_end - hist_start + int(dur / 2), :][::4] = (\n", " padding[::4]\n", " )\n", "\n", " # skip semantic for second half of infill region\n", " sem_codes[hist_end - hist_start + int(dur / 2) : hist_end - hist_start + dur, :][\n", " ::3\n", " ] = padding[::3]\n", "\n", " infill_prefix_latents = torch.from_numpy(\n", " before_song[\"vae_data\"][hist_start:hist_end, :]\n", " )\n", " infill_suffix_latents = torch.from_numpy(\n", " after_song[\"vae_data\"][fut_start:fut_end, :] # 5 sec after gen\n", " )\n", "\n", " gen_cfg = diffusion_gen.DiffusionGenerationConfig(\n", " audio=None,\n", " steps=32,\n", " lyrics=\"instrumental\",\n", " tags=\"\",\n", " text_cfg_coef=4.0,\n", " codec_scale_factor=0.4,\n", " scale_ctx_vector=True,\n", " infill_prefix_latents=infill_prefix_latents,\n", " infill_suffix_latents=infill_suffix_latents,\n", " noise_ctx_level=0.2,\n", " drop_semantic_tokens=True,\n", " )\n", "\n", " for _ in range(5):\n", " request = Request(\n", " id=\"dummy\",\n", " generation_config=gen_cfg,\n", " tokens=sem_codes,\n", " input_tokens_finished=True,\n", " )\n", " result = engine.run_request(request).vae_latents\n", " aud = decode_stream_to_full_audio(torch.concat(result))\n", " output[before_file].append(aud)" ] }, { "cell_type": "code", "execution_count": 5, "id": "b0c491cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('b615c8d2-2285-4adf-a0ef-722dec2b65f1', 'Jazz Fusion')\n" ] }, { "ename": "NameError", "evalue": "name 'get_all_encodings' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[5], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m id_to_genre\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(g)\n\u001b[0;32m----> 4\u001b[0m \u001b[43mget_all_encodings\u001b[49m(g[\u001b[38;5;241m1\u001b[39m])[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mplay()\n", "\u001b[0;31mNameError\u001b[0m: name 'get_all_encodings' is not defined" ] } ], "source": [ "id_to_genre = {v: k for k, v in genre_to_id.items()}\n", "for g in id_to_genre.items():\n", " print(g)\n", " get_all_encodings(g[1])[\"audio\"].play()" ] }, { "cell_type": "code", "execution_count": null, "id": "9884bd5f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 33, "id": "a9b1542f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jazz Fusion and Country\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Blues and Hip-Hop Beat\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Reggae and Classical Piano\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Folk and Rock Ballad\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Ambient Electronic and Electronic\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "id_to_genre = {v: k for k, v in genre_to_id.items()}\n", "fig, axes = plt.subplots(5, 5, figsize=(15, 10), sharex=True, sharey=True)\n", "col = 0\n", "for i in output.items():\n", " # Find the second element of the song pair whose first entry is i[0]\n", " second_elem = next((pair[1] for pair in song_pairs if pair[0] == i[0]), None)\n", " print(f\"{id_to_genre[i[0]]} and {id_to_genre[second_elem]}\")\n", " ax_list = axes[:, col]\n", " col += 1\n", " for j in range(len(i[1])):\n", " i[1][j].play()\n", " ax_list[j].plot(i[1][j].array[0, :])\n", " if j == 0:\n", " ax_list[j].set_title(f\"{id_to_genre[i[0]]} and {id_to_genre[second_elem]}\")\n", " ax_list[j].axvline(\n", " x=i[1][j].array.shape[1] // 3, color=\"g\", linestyle=\"--\", linewidth=1\n", " )\n", " ax_list[j].axvline(\n", " x=2 * i[1][j].array.shape[1] // 3, color=\"g\", linestyle=\"--\", linewidth=1\n", " )\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "id": "1548bf4a", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot([1, 2, 3])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "bb5f42ab", "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[21], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 4\u001b[0m audio_array \u001b[38;5;241m=\u001b[39m output[song_pairs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39marray_float[:\u001b[38;5;241m1000\u001b[39m]\n\u001b[0;32m----> 5\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio_array\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# ax.set_xticks([])\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# ax.set_yticks([])\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# plt.tight_layout(rect=[0, 0, 1, 0.96])\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# plt.show()\u001b[39;00m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1777\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:546\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:546\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:539\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 535\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 536\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel must be scalar or have the same length as the input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 537\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, but found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(label)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_datasets\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m datasets.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 539\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[43mmake_artist\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncy\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 541\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels))\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_kwargs:\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:338\u001b[0m, in \u001b[0;36m_process_plot_var_args._make_line\u001b[0;34m(self, axes, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m 336\u001b[0m kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs} \u001b[38;5;66;03m# Don't modify the original kw.\u001b[39;00m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setdefaults(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getdefaults(kw), kw)\n\u001b[0;32m--> 338\u001b[0m seg \u001b[38;5;241m=\u001b[39m \u001b[43mmlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLine2D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m seg, kw\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/lines.py:421\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[1;32m 419\u001b[0m \u001b[38;5;66;03m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;66;03m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[0;32m--> 421\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_internal_update\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpickradius \u001b[38;5;241m=\u001b[39m pickradius\n\u001b[1;32m 423\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/artist.py:1233\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1228\u001b[0m \u001b[38;5;124;03m Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[1;32m 1229\u001b[0m \u001b[38;5;124;03m errors as if calling `set`.\u001b[39;00m\n\u001b[1;32m 1230\u001b[0m \n\u001b[1;32m 1231\u001b[0m \u001b[38;5;124;03m The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[1;32m 1232\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1233\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_props\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1234\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{cls.__name__}\u001b[39;49;00m\u001b[38;5;124;43m.set() got an unexpected keyword argument \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 1235\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{prop_name!r}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/artist.py:1211\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m 1209\u001b[0m ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n\u001b[0;32m-> 1211\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpchanged\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1212\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/artist.py:425\u001b[0m, in \u001b[0;36mArtist.pchanged\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mpchanged\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 415\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;124;03m Call all of the registered callbacks.\u001b[39;00m\n\u001b[1;32m 417\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;124;03m remove_callback\u001b[39;00m\n\u001b[1;32m 424\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 425\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_callbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpchanged\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/cbook.py:348\u001b[0m, in \u001b[0;36mCallbackRegistry.process\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks[signal]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m: \u001b[38;5;66;03m# Clean up empty dicts\u001b[39;00m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks[signal]\n\u001b[0;32m--> 348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 349\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 350\u001b[0m \u001b[38;5;124;03m Process signal *s*.\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \n\u001b[1;32m 352\u001b[0m \u001b[38;5;124;03m All of the functions registered to receive callbacks on *s* will be\u001b[39;00m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;124;03m called with ``*args`` and ``**kwargs``.\u001b[39;00m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signals \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(5, 5, figsize=(15, 10), sharex=True, sharey=True)\n", "fig.suptitle(\"Wavefaorms (Left Channel)\", fontsize=16)\n", "ax = axes[0, 0]\n", "audio_array = output[song_pairs[0][0]][0].array_float[:1000]\n", "ax.plot(audio_array, linewidth=0.5)\n", "# ax.set_xticks([])\n", "# ax.set_yticks([])\n", "\n", "# if col_idx == 0:\n", "# ax.set_ylabel(song_id[:8], fontsize=8)\n", "# if row_idx == 4:\n", "# ax.set_xlabel(f\"Clip {col_idx+1}\", fontsize=8)\n", "\n", "# plt.tight_layout(rect=[0, 0, 1, 0.96])\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "b82ea0bc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Exception ignored in: >\n", "Traceback (most recent call last):\n", " File \"/home/zoe/.local/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 775, in _clean_thread_parent_frames\n", " def _clean_thread_parent_frames(\n", "KeyboardInterrupt: \n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[15], line 12\u001b[0m\n\u001b[1;32m 9\u001b[0m ax \u001b[38;5;241m=\u001b[39m axes[row_idx, col_idx]\n\u001b[1;32m 10\u001b[0m audio_array \u001b[38;5;241m=\u001b[39m audio\u001b[38;5;241m.\u001b[39marray\n\u001b[0;32m---> 12\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43maudio_array\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xticks([])\n\u001b[1;32m 14\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticks([])\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1777\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:546\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:546\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n\u001b[1;32m 545\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [l[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m result]\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:539\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 535\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 536\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel must be scalar or have the same length as the input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 537\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, but found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(label)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_datasets\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m datasets.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 539\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[43mmake_artist\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mncy\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 540\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 541\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(labels))\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_kwargs:\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(result)\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/axes/_base.py:338\u001b[0m, in \u001b[0;36m_process_plot_var_args._make_line\u001b[0;34m(self, axes, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m 336\u001b[0m kw \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs} \u001b[38;5;66;03m# Don't modify the original kw.\u001b[39;00m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setdefaults(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getdefaults(kw), kw)\n\u001b[0;32m--> 338\u001b[0m seg \u001b[38;5;241m=\u001b[39m \u001b[43mmlines\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLine2D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m seg, kw\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/lines.py:422\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[38;5;66;03m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;66;03m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[1;32m 421\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_update(kwargs)\n\u001b[0;32m--> 422\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpickradius\u001b[49m \u001b[38;5;241m=\u001b[39m pickradius\n\u001b[1;32m 423\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_picker, Number) \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_picker, \u001b[38;5;28mbool\u001b[39m)):\n", "File \u001b[0;32m~/miniconda3/envs/suno10/lib/python3.10/site-packages/matplotlib/lines.py:531\u001b[0m, in \u001b[0;36mLine2D.set_pickradius\u001b[0;34m(self, pickradius)\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mset_pickradius\u001b[39m(\u001b[38;5;28mself\u001b[39m, pickradius):\n\u001b[1;32m 521\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;124;03m Set the pick radius used for containment tests.\u001b[39;00m\n\u001b[1;32m 523\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[38;5;124;03m Pick radius, in points.\u001b[39;00m\n\u001b[1;32m 530\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 531\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pickradius, Real) \u001b[38;5;129;01mor\u001b[39;00m pickradius \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 532\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpick radius should be a distance\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 533\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pickradius \u001b[38;5;241m=\u001b[39m pickradius\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "\n", "fig, axes = plt.subplots(5, 5, figsize=(15, 10), sharex=True, sharey=True)\n", "fig.suptitle(\"Waveforms (Left Channel)\", fontsize=16)\n", "\n", "for row_idx, (song_id, audio_list) in enumerate(output.items()):\n", " for col_idx, audio in enumerate(audio_list):\n", " ax = axes[row_idx, col_idx]\n", " audio_array = audio.array\n", "\n", " ax.plot(audio_array, linewidth=0.5)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", "\n", " if col_idx == 0:\n", " ax.set_ylabel(song_id[:8], fontsize=8)\n", " if row_idx == 4:\n", " ax.set_xlabel(f\"Clip {col_idx+1}\", fontsize=8)\n", "\n", "plt.tight_layout(rect=[0, 0, 1, 0.96])\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "suno10", "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": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 }