{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:38.733308Z",
"iopub.status.busy": "2024-10-12T04:24:38.733172Z",
"iopub.status.idle": "2024-10-12T04:24:39.623503Z",
"shell.execute_reply": "2024-10-12T04:24:39.623049Z",
"shell.execute_reply.started": "2024-10-12T04:24:38.733293Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 |\n",
"| 0 NVIDIA H100 80GB HBM3 On | 00000000:0F:00.0 Off | 0 |\n"
]
}
],
"source": [
"!nvidia-smi | grep NVIDIA | head -2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:39.624514Z",
"iopub.status.busy": "2024-10-12T04:24:39.624154Z",
"iopub.status.idle": "2024-10-12T04:24:39.733700Z",
"shell.execute_reply": "2024-10-12T04:24:39.733186Z",
"shell.execute_reply.started": "2024-10-12T04:24:39.624497Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"h100-ord01-03-37\n"
]
}
],
"source": [
"!echo $HOSTNAME"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:39.735354Z",
"iopub.status.busy": "2024-10-12T04:24:39.735036Z",
"iopub.status.idle": "2024-10-12T04:24:39.737408Z",
"shell.execute_reply": "2024-10-12T04:24:39.737018Z",
"shell.execute_reply.started": "2024-10-12T04:24:39.735337Z"
}
},
"outputs": [],
"source": [
"# !aws s3 cp s3://suno-data/georg/tmp/model.pt /app/suno/tmp/2b.pt\n",
"# !aws s3 cp s3://suno-data/georg/tmp/model_c25.pt /app/suno/tmp/2b_c25.pt\n",
"# !aws s3 cp s3://suno-data/georg/tmp/model_s250c1.pt /app/suno/tmp/2b_s250c1.pt\n",
"# !aws s3 cp s3://suno-data/georg/tmp/model_s250c5.pt /app/suno/tmp/2b_s250c5.pt\n",
"# !aws s3 cp s3://suno-data/georg/tmp/model_s250c25.pt /app/suno/tmp/2b_s250c25.pt\n",
"\n",
"# !aws s3 cp s3://suno-data/georg/tmp/model_noSem_s250c25.pt /app/suno/tmp/2b_noSem_s250c25.pt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:39.738175Z",
"iopub.status.busy": "2024-10-12T04:24:39.738046Z",
"iopub.status.idle": "2024-10-12T04:24:39.778816Z",
"shell.execute_reply": "2024-10-12T04:24:39.778447Z",
"shell.execute_reply.started": "2024-10-12T04:24:39.738161Z"
}
},
"outputs": [],
"source": [
"# ## fix 13b\n",
"# from collections import OrderedDict\n",
"# import os\n",
"# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
"# import torch\n",
"# sd = torch.load(\"/app/suno/checkpoints/2024-05-09_14-19-24/last_ckpt_infer.pt\")\n",
"# sd[\"model_args\"][\"embed_scale_factor\"] = 1.0\n",
"# sd[\"model_args\"][\"activation_f\"] = \"gelu\"\n",
"# sd[\"model_args\"][\"use_qk_norm\"] = False\n",
"# del sd[\"model_args\"][\"attention_type\"]\n",
"# sd[\"model\"] = OrderedDict({k: v.to(torch.bfloat16) for k, v in sd[\"model\"].items()})\n",
"# torch.save(sd, \"/app/suno/checkpoints/2024-05-09_14-19-24/last_ckpt_infer__fix.pt\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:39.779603Z",
"iopub.status.busy": "2024-10-12T04:24:39.779469Z",
"iopub.status.idle": "2024-10-12T04:24:39.817590Z",
"shell.execute_reply": "2024-10-12T04:24:39.817220Z",
"shell.execute_reply.started": "2024-10-12T04:24:39.779589Z"
}
},
"outputs": [],
"source": [
"# !aws s3 cp /app/suno/checkpoints/2024-05-09_14-19-24/last_ckpt_infer__fix.pt s3://suno-data/georg/tmp/model_13b.pt"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:39.818311Z",
"iopub.status.busy": "2024-10-12T04:24:39.818191Z",
"iopub.status.idle": "2024-10-12T04:24:41.049038Z",
"shell.execute_reply": "2024-10-12T04:24:41.048400Z",
"shell.execute_reply.started": "2024-10-12T04:24:39.818300Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"7\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:41.050213Z",
"iopub.status.busy": "2024-10-12T04:24:41.049831Z",
"iopub.status.idle": "2024-10-12T04:24:45.072837Z",
"shell.execute_reply": "2024-10-12T04:24:45.072146Z",
"shell.execute_reply.started": "2024-10-12T04:24:41.050194Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WILL USE FLASH ATTN: True\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tony/Work/glockenspiel/suno_utils/suno_utils/gpt/engine.py:199: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
" @autocast()\n"
]
}
],
"source": [
"from suno_utils.gpt.chirp_v2_5 import (\n",
" preload_codec_models,\n",
" _get_model_if_needed,\n",
" GenerationConfig,\n",
" codec_decode_stream_to_full_audio,\n",
")\n",
"from suno_utils.gpt.generation_engine import (\n",
" make_prompt,\n",
" align_codes,\n",
" make_request,\n",
")\n",
"from suno_utils.gpt.engine import Engine, Request, Job\n",
"from suno_utils.gpt.prompt import Prompt\n",
"\n",
"N_BATCH = 2\n",
"MAX_STREAMS = N_BATCH * 4"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:24:45.073979Z",
"iopub.status.busy": "2024-10-12T04:24:45.073719Z",
"iopub.status.idle": "2024-10-12T04:25:36.012654Z",
"shell.execute_reply": "2024-10-12T04:25:36.012019Z",
"shell.execute_reply.started": "2024-10-12T04:24:45.073962Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tony/Work/glockenspiel/suno_utils/suno_utils/tasks/dac_2c_12cb.py:75: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" sd = load_f(checkpoint_filepath)\n",
"/home/tony/anaconda3/envs/suno_env/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
" warnings.warn(\n",
"/home/tony/Work/glockenspiel/suno_utils/suno_utils/gpt/generation.py:94: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" checkpoint = torch.load(str(ckpt_path), mmap=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"model loaded: /app/suno/data/dpo/models/model_30b_ft_t5.pt\n",
"model loaded: 32020.4M params\n",
"Allocated 16800 blocks, 8.20 GB\n",
"GPTConfig(n_layer=60, n_head=56, n_kv_head=4, d_head=128, block_size=8832, bias=False, text_vocab_size=60032, text_codebook_size=60001, text_pad_token=60001, semantic_vocab_size=4032, semantic_codebook_size=4000, semantic_n_codebooks=1, semantic_pad_token=4000, semantic_infer_token=4001, semantic_mask_token=4002, semantic_cover_token=4003, semantic_artist_token=4004, semantic_future_token=4005, semantic_rate_hz=25, semantic_shift_factor=50, coarse_vocab_size=2112, coarse_codebook_size=2048, coarse_n_codebooks=12, coarse_pad_token=2048, coarse_infer_token=2049, coarse_rate_hz=25, coarse_shift_factor=5, t_text=2560, t_audio=6272, t_memmap=6016, use_sin_pos_emb=False, use_rotary_pos_emb=True, rope_theta=500000, attention_type='torch', use_qk_norm=True, activation_f='silu', embed_scale_factor=10.0, attention_sliding_window_size=1024, global_every_n_layers=1)\n"
]
}
],
"source": [
"_ = preload_codec_models(\"/app/suno/models/chirp_v2/dac_2c_25x12.pt\")\n",
"gpt_ckpt_path = _get_model_if_needed(\n",
"# \"s3://suno-data/georg/checkpoints/chirp_v2_5/7b_ft.pt\"\n",
"# \"/app/suno/checkpoints/2024-05-09_14-19-24/last_ckpt_infer__fix.pt\" # 13b\n",
"# \"/app/suno/data/dpo/models/model_30b_150k.pt\" # 30b\n",
"# \"/app/suno/checkpoints/2024-07-01_14-55-52/last_ckpt_infer.pt\" # 30b 2h 4l\n",
"# \"/app/suno/checkpoints/2024-07-08_15-35-27/last_ckpt_infer.pt\" # 30b ft\n",
"# \"/app/suno/checkpoints/2024-07-11_20-25-42/last_ckpt_infer.pt\" # 30b ft r2\n",
"# \"/app/suno/checkpoints/2024-07-16_03-21-53/10k_ckpt_infer.pt\" # 30b ft r3\n",
"# \"/app/suno/checkpoints/2024-07-20_01-08-38/last_ckpt_infer.pt\" # 30b ft r4\n",
"# \"/app/suno/data/dpo/models/model_30b_fix_ft2_20k.pt\" # 30b t1\n",
"# \"/app/suno/checkpoints/2024-08-23_13-28-02/last_ckpt_infer.pt\" # 7b delay 1\n",
"# \"/app/suno/checkpoints/2024-08-07_19-38-28/last_ckpt_infer.pt\" # 7b\n",
"# \"/app/suno/checkpoints/2024-07-24_20-16-51/last_ckpt_infer.pt\" # 30b ft r5 half sem delay\n",
"# \"/app/suno/checkpoints/2024-07-26_15-28-26/last_ckpt_infer.pt\" # 30b ft r6 32 delay\n",
"# \"/app/suno/checkpoints/2024-07-27_18-21-41/last_ckpt_infer.pt\" # 30b ft r7 14 delay\n",
"# \"/app/suno/checkpoints/2024-07-31_04-15-07/last_ckpt_infer.pt\" # 30b ft r8 5 sem delay\n",
"# \"/app/suno/data/dpo/models/model_30b_ft_t1_5k.pt\" # 30b ft t1 2k step\n",
"# \"/app/suno/tmp/2b_plus.pt\"\n",
"# \"/app/suno/tmp/2b_musicfm.pt\"\n",
"# \"/app/suno/tmp/2b.pt\",\n",
"# \"/app/suno/tmp/2b_c25.pt\",\n",
"# \"/app/suno/tmp/2b_s250c1.pt\",\n",
"# \"/app/suno/tmp/2b_s250c5.pt\",\n",
"# \"/app/suno/tmp/2b_s250c25.pt\",\n",
"# \"/app/suno/tmp/2b_noSem_s250c25.pt\",\n",
"# \"/app/suno/data/dpo/models/model_30b_ft_t3.pt\" # 30b t3 v2\n",
" \"/app/suno/data/dpo/models/model_30b_ft_t5.pt\" # 30b t5\n",
")\n",
"engine = Engine(\n",
" gpt_ckpt_path,\n",
" \"/app/suno/models/chirp_v2/tokenizer_60k.json\",\n",
" max_sequences=MAX_STREAMS,\n",
" compile=False,\n",
")\n",
"model = engine.model\n",
"cfg = model.config\n",
"tokenizer = engine.tokenizer\n",
"\n",
"print(cfg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic preds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:25:36.013648Z",
"iopub.status.busy": "2024-10-12T04:25:36.013480Z",
"iopub.status.idle": "2024-10-12T04:25:36.018526Z",
"shell.execute_reply": "2024-10-12T04:25:36.018056Z",
"shell.execute_reply.started": "2024-10-12T04:25:36.013632Z"
}
},
"outputs": [],
"source": [
"text = \"\"\"\n",
"[Verse 1]\n",
"Almost Heaven, West Virginia\n",
"Blue Ridge Mountains, Shenandoah River\n",
"Life is old there, older than the trees\n",
"Younger than the mountains, growing like a breeze\n",
"\n",
"[Chorus]\n",
"Country roads, take me home\n",
"To the place I belong\n",
"West Virginia, mountain mama\n",
"Take me home, country roads\n",
"\"\"\"\n",
"\n",
"gconf = GenerationConfig(\n",
" text=text,\n",
"# text_tags=\"dark epic trailer music, violin, vocalizations, female\",\n",
"# text_tags=\"pop, power ballad, female, violin\",\n",
" text_tags=\"bluegrass, female\",\n",
"# text_tags=\"sea shanty, sea shanty, sea shanty\",\n",
" \n",
" cfg_coef=1.3,\n",
" cfg_coef_tags=2,\n",
" n_repeat_tags=3,\n",
"\n",
" # text_start_control_tags=\"{start;start:0;vocals:intro}\",\n",
" # text_end_control_tags=\"{end}\",\n",
" cfg_coef_neg_tags=-2,\n",
" text_neg_tags=\"repetitive, loop\",\n",
" \n",
"# temp_semantic=0.85,\n",
"# temp_coarse=0.9,\n",
"# top_k_semantic=None,\n",
"# top_k_coarse=None,\n",
"# top_p_semantic=0.95,\n",
"# top_p_coarse=0.98,\n",
" \n",
" n_batch=1,\n",
" min_eos_p=0.1,\n",
"# allow_eos=False,\n",
" min_text_offset=0,\n",
" eos_pad_duration_s=0,\n",
" max_gen_duration_s=60,\n",
")\n",
"\n",
"request = make_request(\"0\", gconf, cfg, tokenizer)\n",
"in_arr = request.streams[0].prompt\n",
"# prompt.visualize(in_arr)\n",
"# prompt = Prompt(gconf, cfg, tokenizer)\n",
"# prompt.visualize(in_arr)\n",
"# for n in range(4):\n",
"# print(\"--\", n, \"--\")\n",
"# print(tokenizer.decode(request.streams[n].prompt[0][:-1].tolist()))\n",
"# print()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:25:36.019307Z",
"iopub.status.busy": "2024-10-12T04:25:36.019161Z",
"iopub.status.idle": "2024-10-12T04:26:39.047577Z",
"shell.execute_reply": "2024-10-12T04:26:39.047001Z",
"shell.execute_reply.started": "2024-10-12T04:25:36.019293Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([240, 240, 240, 240, 240, 240, 240, 240, 240, 240], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([480, 480, 480, 480, 480, 480, 480, 480, 480, 480], dtype=torch.int32) future blocks. Currently 16560 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1383it [00:51, 26.47it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 found EOS at step 1397\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1491it [00:55, 26.48it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 completed at step 1502\n",
"Job 1 completed. ITL: 37.0ms, TTL: 55641.1ms, TTFT: 208.2ms\n",
"Job 1 completed at step 1502\n",
"Job 1 completed. ITL: 37.0ms, TTL: 55679.0ms, TTFT: 208.2ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1605it [00:59, 27.11it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed. ITL: 36.9ms, TTL: 59346.3ms, TTFT: 408.5ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\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"
}
],
"source": [
"requests = [\n",
" make_request(f\"{i}\", gconf, engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"# print(requests[0])\n",
"jobs = engine.run_request(requests, tqdm_enabled=True)\n",
"\n",
"for job in jobs:\n",
" stream = engine.token_generator(job)\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:26:39.048503Z",
"iopub.status.busy": "2024-10-12T04:26:39.048338Z",
"iopub.status.idle": "2024-10-12T04:27:41.750990Z",
"shell.execute_reply": "2024-10-12T04:27:41.750357Z",
"shell.execute_reply.started": "2024-10-12T04:26:39.048486Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([240, 240, 240, 240, 240, 240, 240, 240, 240, 240], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([480, 480, 480, 480, 480, 480, 480, 480, 480, 480], dtype=torch.int32) future blocks. Currently 16560 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1331it [00:49, 26.42it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 found EOS at step 1345\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1439it [00:53, 26.41it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 completed at step 1450\n",
"Job 1 completed. ITL: 37.1ms, TTL: 53769.5ms, TTFT: 180.6ms\n",
"Job 1 completed at step 1450\n",
"Job 1 completed. ITL: 37.1ms, TTL: 53807.5ms, TTFT: 180.6ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1496it [00:55, 27.40it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 found EOS at step 1502\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1605it [00:59, 27.13it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed. ITL: 36.9ms, TTL: 59288.4ms, TTFT: 317.0ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
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"source": [
"requests = [\n",
" make_request(f\"{i}\", gconf, engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"# print(requests[0])\n",
"jobs = engine.run_request(requests, tqdm_enabled=True)\n",
"\n",
"for job in jobs:\n",
" stream = engine.token_generator(job)\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "code",
"execution_count": 12,
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"text = \"\"\"\n",
"[Intro]\n",
"I can feel the heat risin'\n",
"Everything is on fire\n",
"Today's a painful re-\n",
"Minder of why\n",
"It can only get brighter\n",
"The further you put it behind ya\n",
"But right now I'm on the\n",
"Inside lookin' out, ‘cause—\n",
"\n",
"[Chorus]\n",
"I'm standin' in the flames\n",
"And it’s a beautiful kind of pain\n",
"Settin' fire to yesterday\n",
"To find the light, find the light, find the light\n",
"Standin' in the flames\n",
"And it’s a beautiful kind of pain\n",
"Settin' fire to yesterday\n",
"Find the light, find the light, find the light\n",
"\n",
"[Verse 1]\n",
"Yesterday was the tornado warning, today's like the morning after\n",
"Your world is torn in half, you wake in its wake\n",
"To start the mourning process and rebuilding, you're still a work in progress\n",
"Today's a whole new chapter, it's like an enormous asthma\n",
"Thunderstorm has passed ya, you weathered it and poked its\n",
"Eye out with the thornbush that ya used to smell the roses\n",
"Stopped to inhale, can't even tell your nose is stuffed\n",
"So focused on the bright side then you floor the gas pedal\n",
"And hit the corner faster, more assertive, never looking back\n",
"May hit the curb, but every day's a new learning curve as ya\n",
"Steer through life, sometimes you might not wanna swerve but you have to\n",
"To avert a disaster, lucky no permanent damage\n",
"‘Cause they hurt you so ba\n",
"\"\"\"\n",
"\n",
"gconf = GenerationConfig(\n",
" text=text,\n",
" text_tags=\"Rap, In English, USA, Midwest Rap, Detroit Rap, Alternative, Hip-Hop\",\n",
"\n",
" cfg_coef=1.3,\n",
" cfg_coef_tags=2,\n",
" n_repeat_tags=3,\n",
"\n",
"# temp_semantic=0.8,\n",
"# temp_coarse=1,\n",
"# top_k_semantic=1000,\n",
"# top_k_coarse=100,\n",
"# top_p_semantic=None,\n",
"# top_p_coarse=None,\n",
" \n",
"# text_start_control_tags=\"{start;start:0;vocals:intro}\",\n",
"# text_end_control_tags=\"{end}\",\n",
"# cfg_coef_neg_tags=-2,\n",
"# text_neg_tags=\"repetitive, loop\",\n",
"\n",
" n_batch=1,\n",
" min_eos_p=0.1,\n",
" min_text_offset=0,\n",
" eos_pad_duration_s=0,\n",
" max_gen_duration_s=60*2,\n",
")"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([240, 240, 240, 240, 240, 240, 240, 240, 240, 240], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([480, 480, 480, 480, 480, 480, 480, 480, 480, 480], dtype=torch.int32) future blocks. Currently 16560 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 2144it [01:20, 26.44it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 found EOS at step 2143\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 2252it [01:24, 26.50it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 completed at step 2248\n",
"Job 1 completed. ITL: 37.6ms, TTL: 84614.6ms, TTFT: 185.3ms\n",
"Job 1 completed at step 2248\n",
"Job 1 completed. ITL: 37.7ms, TTL: 84652.7ms, TTFT: 185.3ms\n",
"Job 0 found EOS at step 2253\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 2358it [01:28, 26.66it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed at step 2358\n",
"Job 0 completed. ITL: 37.5ms, TTL: 88571.9ms, TTFT: 324.3ms\n"
]
},
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"\n"
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"source": [
"requests = [\n",
" make_request(f\"{i}\", gconf, engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"# print(requests[0])\n",
"jobs = engine.run_request(requests, tqdm_enabled=True)\n",
"\n",
"for job in jobs:\n",
" stream = engine.token_generator(job)\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom Input Array"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
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"name": "stderr",
"output_type": "stream",
"text": [
"/home/tony/Work/glockenspiel/suno_utils/suno_utils/tasks/mert_25.py:148: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" sd = _torch_load_p(checkpoint_filepath)\n"
]
}
],
"source": [
"import numpy as np\n",
"from suno_utils.audio import Audio\n",
"from suno_utils.gpt.chirp_v2_5 import (\n",
" codec_encode, codec_decode, preload_semantic_models, semantic_encode\n",
")\n",
"_ = preload_semantic_models(\n",
" checkpoint_filepath=\"/app/suno/models/chirp_v2/mert_25.pt\",\n",
" centroids_filepath=\"/app/suno/models/chirp_v2/mert_25_2x4k.npy\",\n",
" device=\"cpu\",\n",
")\n",
"\n",
"# # MusicFM\n",
"# from suno_utils.tasks.musicfm_v2 import (\n",
"# preload_models as preload_semantic_models, \n",
"# encode as semantic_encode\n",
"# )\n",
"# _ = preload_semantic_models(\n",
"# centroids_filepath=\"s3://suno-data/minz/models/musicfm_concat_centroids_2s_8192.npy\",\n",
"# )\n",
"\n",
"def process_audio(audio, cfg):\n",
" audio = audio.normalize_volume(-16)\n",
" sem_arr = semantic_encode(audio, device=\"cpu\")\n",
" coarse_arr = codec_encode(audio)\n",
" n_frames = min(sem_arr.shape[0], coarse_arr.shape[0])\n",
" sem_arr = sem_arr[:n_frames, : cfg.semantic_n_codebooks]\n",
" coarse_arr = coarse_arr[:n_frames, : cfg.coarse_n_codebooks]\n",
"\n",
" a_arr = np.concatenate([sem_arr, coarse_arr], axis=-1)\n",
" return a_arr\n",
"\n",
"def load_audio(fp):\n",
" return Audio.from_file(fp, n_channels=2, sample_rate=48_000, byte_width=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Continue"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:29:22.987853Z",
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"\n",
"\n"
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""
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],
"source": [
"audio = Audio.from_file(\"audios/50cent.wav\", n_channels=2, sample_rate=48_000, byte_width=2) # 50 cent\n",
"audio = audio.get_segment(from_s=24.99, to_s=45)\n",
"# audio = Audio.from_file(\"tmp/eminem_doctor.wav\", n_channels=2, sample_rate=48_000, byte_width=2) # eminem doctor\n",
"history_text = \"\"\"\n",
"Welcome to the candy shop\n",
"\n",
"Yeah, uh-huh\n",
"So seductive\n",
"\n",
"I take you to the candy shop\n",
"I let you lick the lollipop\n",
"Go 'head, girl, don't you stop\n",
"\"\"\"\n",
"text = \"\"\"\n",
"Almost Heaven, West Virginia\n",
"Blue Ridge Mountains, Shenandoah River\n",
"Life is old there, older than the trees\n",
"Younger than the mountains, growing like a breeze\n",
"\n",
"Country roads, take me home\n",
"To the place I belong\n",
"West Virginia, mountain mama\n",
"Take me home, country roads\n",
"\"\"\"\n",
"in_history_arr = process_audio(audio, cfg)\n",
"audio.play()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"# codec_decode(codec_encode(audio)).play()"
]
},
{
"cell_type": "code",
"execution_count": 17,
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"execution": {
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"outputs": [
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([300, 300, 300, 300, 300, 300, 300, 300, 300, 300], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([600, 600, 600, 600, 600, 600, 600, 600, 600, 600], dtype=torch.int32) future blocks. Currently 16500 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1079it [00:40, 26.42it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 found EOS at step 1089\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1105it [00:41, 26.41it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed. ITL: 37.8ms, TTL: 42027.9ms, TTFT: 400.5ms\n",
"Job 1 completed. ITL: 37.8ms, TTL: 41848.5ms, TTFT: 257.8ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
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"data": {
"text/html": [
"\n",
"\n"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gconf = GenerationConfig(\n",
" text=text,\n",
"# text_tags=\"bluegrass female\",\n",
" history_arr=in_history_arr,\n",
" history_text=history_text,\n",
" n_batch=1,\n",
" cfg_coef=1.2,\n",
" cfg_coef_tags=0,\n",
" max_gen_duration_s=40,\n",
" min_text_offset=0,\n",
" eos_pad_duration_s=0,\n",
")\n",
"\n",
"request = make_request(\"0\", gconf, cfg, tokenizer)\n",
"in_arr = request.streams[0].prompt\n",
"\n",
"prompt = Prompt(gconf, cfg, tokenizer)\n",
"prompt.visualize(in_arr)\n",
"requests = [\n",
" make_request(f\"{i}\", gconf.modify(n_batch=1), engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"for job in engine.run_request(requests, tqdm_enabled=True):\n",
" stream = engine.token_generator(job)\n",
"\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Oracle"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:09.330113Z",
"iopub.status.busy": "2024-10-12T04:30:09.329959Z",
"iopub.status.idle": "2024-10-12T04:30:09.332723Z",
"shell.execute_reply": "2024-10-12T04:30:09.332226Z",
"shell.execute_reply.started": "2024-10-12T04:30:09.330098Z"
}
},
"outputs": [],
"source": [
"# audio = load_audio(\"tmp/50cent.wav\").get_segment(from_s=24.99, to_s=45)\n",
"# text = \"\"\"\n",
"# Welcome to the candy shop\n",
"\n",
"# Yeah, uh-huh\n",
"# So seductive\n",
"\n",
"# I take you to the candy shop\n",
"# I let you lick the lollipop\n",
"# Go 'head, girl, don't you stop\n",
"# \"\"\"\n",
"# in_oracle_arr = process_audio(audio, cfg)\n",
"# audio.play()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:09.333529Z",
"iopub.status.busy": "2024-10-12T04:30:09.333386Z",
"iopub.status.idle": "2024-10-12T04:30:13.230522Z",
"shell.execute_reply": "2024-10-12T04:30:13.229873Z",
"shell.execute_reply.started": "2024-10-12T04:30:09.333515Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"audio = load_audio(\"audios/halo.wav\").get_segment(from_s=49.99, to_s=70)\n",
"text = \"\"\"\n",
"It's like I've been awakened\n",
"Every rule, I had you breakin'\n",
"The risk that I'm takin'\n",
"I'm never gonna shut you out\n",
"\n",
"Everywhere I'm lookin' now\n",
"I'm surrounded by your embrace\n",
"Baby, I can see your halo\n",
"\"\"\"\n",
"in_oracle_arr = process_audio(audio, cfg)\n",
"# in_oracle_arr[:,:1] = cfg.semantic_mask_token\n",
"audio.normalize_volume().play()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:13.231457Z",
"iopub.status.busy": "2024-10-12T04:30:13.231299Z",
"iopub.status.idle": "2024-10-12T04:30:13.234054Z",
"shell.execute_reply": "2024-10-12T04:30:13.233564Z",
"shell.execute_reply.started": "2024-10-12T04:30:13.231441Z"
}
},
"outputs": [],
"source": [
"# codec_decode(codec_encode(audio)[:,:6]).normalize_volume().play()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:13.234907Z",
"iopub.status.busy": "2024-10-12T04:30:13.234749Z",
"iopub.status.idle": "2024-10-12T04:30:35.291098Z",
"shell.execute_reply": "2024-10-12T04:30:35.290488Z",
"shell.execute_reply.started": "2024-10-12T04:30:13.234893Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([60, 60, 60, 60, 60, 60, 60, 60, 60, 60], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([120, 120, 120, 120, 120, 120, 120, 120, 120, 120], dtype=torch.int32) future blocks. Currently 16740 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 505it [00:17, 28.91it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 1 found EOS at step 501\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 605it [00:20, 29.08it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed. ITL: 34.4ms, TTL: 20838.2ms, TTFT: 110.0ms\n",
"Job 1 completed. ITL: 34.4ms, TTL: 20803.3ms, TTFT: 77.5ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\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"
}
],
"source": [
"gconf = GenerationConfig(\n",
" text=text,\n",
"# text_tags=\"bluegrass female\",\n",
" oracle_arr=in_oracle_arr[:,:1],\n",
" n_batch=1,\n",
" cfg_coef=1, #.3,\n",
" cfg_coef_tags=0,\n",
" cfg_coef_neg_tags=0,\n",
" max_gen_duration_s=20,\n",
" min_text_offset=0,\n",
" eos_pad_duration_s=0,\n",
")\n",
"\n",
"request = make_request(\"0\", gconf, cfg, tokenizer)\n",
"in_arr = request.streams[0].prompt\n",
"\n",
"prompt = Prompt(gconf, cfg, tokenizer)\n",
"\n",
"requests = [\n",
" make_request(f\"{i}\", gconf.modify(n_batch=1), engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"for job in engine.run_request(requests, tqdm_enabled=True):\n",
" stream = engine.token_generator(job)\n",
"\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Covers"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:35.292049Z",
"iopub.status.busy": "2024-10-12T04:30:35.291882Z",
"iopub.status.idle": "2024-10-12T04:30:42.274354Z",
"shell.execute_reply": "2024-10-12T04:30:42.273704Z",
"shell.execute_reply.started": "2024-10-12T04:30:35.292034Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# audio = Audio.from_file(\"tmp/georg_country.mp3\", n_channels=2).get_segment(to_s=60.01)\n",
"# audio = Audio.from_file(\"tmp/georg_bday.mp3\", n_channels=2).get_segment(to_s=60.01)\n",
"# audio = load_audio(\"audios/halo.wav\").get_segment(from_s=49.99, to_s=70)\n",
"audio = load_audio(\"audios/MurphysLawSchool_short_super.mp3\")\n",
"audio.play()\n",
"in_cover_arr = process_audio(audio, cfg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:42.275323Z",
"iopub.status.busy": "2024-10-12T04:30:42.275156Z",
"iopub.status.idle": "2024-10-12T04:30:42.280486Z",
"shell.execute_reply": "2024-10-12T04:30:42.279976Z",
"shell.execute_reply.started": "2024-10-12T04:30:42.275306Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4460, 13)\n"
]
}
],
"source": [
"test_npz = np.load(\"audios/4bd01278-b5a7-491d-8fa7-58cbefa09873.npz\")\n",
"in_cover_arr = test_npz[\"v3.0_raw\"]\n",
"print(in_cover_arr.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:30:42.281287Z",
"iopub.status.busy": "2024-10-12T04:30:42.281140Z",
"iopub.status.idle": "2024-10-12T04:31:45.021877Z",
"shell.execute_reply": "2024-10-12T04:31:45.021318Z",
"shell.execute_reply.started": "2024-10-12T04:30:42.281272Z"
}
},
"outputs": [
{
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2 requests\n",
"Added job 0\n",
"Job 0 predicts tensor([960, 960, 960, 960, 960, 960, 960, 960, 960, 960], dtype=torch.int32) future blocks. Currently 16800 free blocks.\n",
"Added job 1\n",
"Job 1 predicts tensor([1920, 1920, 1920, 1920, 1920, 1920, 1920, 1920, 1920, 1920],\n",
" dtype=torch.int32) future blocks. Currently 15840 free blocks.\n",
"Running 2 requests\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Engine step: 1605it [00:57, 28.16it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Job 0 completed. ITL: 35.5ms, TTL: 57624.0ms, TTFT: 1283.9ms\n",
"Job 1 completed. ITL: 35.5ms, TTL: 57006.0ms, TTFT: 667.2ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\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"
}
],
"source": [
"# text = \"\"\"\n",
"# Happy birthday to you\n",
"# Happy birthday to you\n",
"# Happy birthday dear Margu\n",
"# Happy birthday to you\n",
"\n",
"# Happy birthday to you\n",
"# Happy birthday to you\n",
"# Happy birthday dear Keenan\n",
"# Happy birthday to you\n",
"\n",
"# Happy birthday to you\n",
"# Happy birthday to you\n",
"# Happy birthday dear Mikey\n",
"# Happy birthday to you\n",
"\n",
"# Happy birthday to you\n",
"# Happy birthday to you\n",
"# Happy birthday dear Suno\n",
"# Happy birthday to you\n",
"\n",
"# Happy birthday to you\n",
"# Happy birthday to you\n",
"# I don't know what I'm gonna do next\n",
"# Happy birthday to you\n",
"# \"\"\"\n",
"\n",
"# text = \"\"\"\n",
"# [Verse 1]\n",
"# Almost Heaven, West Virginia\n",
"# Blue Ridge Mountains, Shenandoah River\n",
"# Life is old there, older than the trees\n",
"# Younger than the mountains, growing like a breeze\n",
"\n",
"# [Chorus]\n",
"# Country roads, take me home\n",
"# To the place I belong\n",
"# West Virginia, mountain mama\n",
"# Take me home, country roads\n",
"\n",
"# [Verse 2]\n",
"# All my memories gather 'round her\n",
"# Miner's lady, stranger to blue water\n",
"# Dark and dusty, painted on the sky\n",
"# Misty taste of moonshine, teardrop in my eye\n",
"# \"\"\"\n",
"\n",
"# text = \"\"\"\n",
"# been awakened\n",
"# Every rule, I had you breakin'\n",
"# The risk that I'm takin'\n",
"# I'm never gonna shut you out\n",
"\n",
"# [Chorus]\n",
"# Everywhere I'm lookin' now\n",
"# I'm surrounded by your embrace\n",
"# Baby, I can see your halo\n",
"# \"\"\"\n",
"\n",
"text = \"\"\"\n",
"feel the breeze that's calling me\n",
" golden skies above the sea\n",
" endless summer chasing dreams \n",
"yeah you and i we werere wild and free \n",
"\n",
"we chillin out just riding the vibe under the stars you by my side it feels so right the night is ours you and me are shooting stars \n",
"\n",
"we'll never fade the nights into days a summer will'll always stay \n",
"\n",
"on the sand dunes we run we fly \n",
"burning bright beneath the moonlit sky \n",
"hold me close don't let this go \n",
"\n",
"drifting closer side by side feel us fading with the tide \n",
"every glance every touch \n",
"makes me feel like it's enough \n",
"\n",
"we were chillining out just riding the vibe under the stars you by my side feels so right the night is ours you and me i' shooting stars \n",
"\n",
"we'll never fade the nights into days our summer will always stay \n",
"\n",
"on the sand dunes we run we fly \n",
"building bright beneath the moonlit sky every come don let this go \n",
"the d on the sand dunes we'll be writing our story \n",
"\n",
"every moment is a memory of glory on the sand dun run we fly \n",
"when the winds blow in we sway we sway to the shifting sands we sway we sway \n",
"\n",
"we'll never fade the nights and the days a summer will always stay the moonit sky hearts \n",
"don't let this go on the sand dun well be writing our story every moment is a memory of glory on the sand dun above we fly on dun\n",
"\"\"\"\n",
"\n",
"gconf = GenerationConfig(\n",
" text=text,\n",
" text_tags=\"female, k-pop\",\n",
" cover_arr=in_cover_arr[:3000, :],\n",
" n_batch=1,\n",
" cfg_coef=1.0,\n",
" cfg_coef_tags=2,\n",
" max_gen_duration_s=60,\n",
" min_text_offset=0,\n",
" eos_pad_duration_s=0,\n",
" n_repeat_tags=3\n",
")\n",
"\n",
"request = make_request(\"0\", gconf, cfg, tokenizer)\n",
"in_arr = request.streams[0].prompt\n",
"\n",
"prompt = Prompt(gconf, cfg, tokenizer)\n",
"prompt.visualize(in_arr, compress=True)\n",
"\n",
"assert(N_BATCH * 4 <= 16)\n",
"requests = [\n",
" make_request(f\"{i}\", gconf.modify(n_batch=1), engine.model.config, engine.tokenizer)\n",
" for i in range(N_BATCH)\n",
"]\n",
"for job in engine.run_request(requests, tqdm_enabled=True):\n",
" stream = engine.token_generator(job)\n",
"\n",
" audio = codec_decode_stream_to_full_audio(align_codes(stream, cfg))\n",
" audio.play()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:45.022809Z",
"iopub.status.busy": "2024-10-12T04:31:45.022649Z",
"iopub.status.idle": "2024-10-12T04:31:45.025688Z",
"shell.execute_reply": "2024-10-12T04:31:45.025227Z",
"shell.execute_reply.started": "2024-10-12T04:31:45.022792Z"
}
},
"outputs": [],
"source": [
"test_npz = np.load(\"/home/tony/dpo_batch_iter_0.npz\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:45.026674Z",
"iopub.status.busy": "2024-10-12T04:31:45.026325Z",
"iopub.status.idle": "2024-10-12T04:31:45.067273Z",
"shell.execute_reply": "2024-10-12T04:31:45.066838Z",
"shell.execute_reply.started": "2024-10-12T04:31:45.026657Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X\n",
"Y\n",
"loss_start_index_list\n"
]
}
],
"source": [
"for k in test_npz.keys():\n",
" print(k)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:45.067962Z",
"iopub.status.busy": "2024-10-12T04:31:45.067828Z",
"iopub.status.idle": "2024-10-12T04:31:47.162914Z",
"shell.execute_reply": "2024-10-12T04:31:47.162345Z",
"shell.execute_reply.started": "2024-10-12T04:31:45.067949Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(14, 8832)\n"
]
},
{
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# concat generated to prompt and visualize\n",
"import numpy as np\n",
"\n",
"# in_arr = requests[0].streams[0].prompt\n",
"# generated = np.stack([t.cpu().numpy() for t in job.generated_tokens]).T\n",
"# print(generated.shape)\n",
"# arr = np.concatenate([in_arr, generated], axis=1)\n",
"\n",
"arr = test_npz[\"X\"][0, :]\n",
"print(arr.shape)\n",
"prompt.visualize(arr)\n",
"prompt.visualize(arr, compress=True)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:47.163870Z",
"iopub.status.busy": "2024-10-12T04:31:47.163707Z",
"iopub.status.idle": "2024-10-12T04:31:49.208376Z",
"shell.execute_reply": "2024-10-12T04:31:49.207829Z",
"shell.execute_reply.started": "2024-10-12T04:31:47.163854Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(14, 1605)\n",
"(14, 5043)\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"in_arr = requests[0].streams[0].prompt\n",
"generated = np.stack([t.cpu().numpy() for t in job.generated_tokens]).T\n",
"print(generated.shape)\n",
"arr = np.concatenate([in_arr, generated], axis=1)\n",
"print(arr.shape)\n",
"prompt.visualize(arr)\n",
"prompt.visualize(arr, compress=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Artist"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:49.209362Z",
"iopub.status.busy": "2024-10-12T04:31:49.209191Z",
"iopub.status.idle": "2024-10-12T04:31:56.733938Z",
"shell.execute_reply": "2024-10-12T04:31:56.733262Z",
"shell.execute_reply.started": "2024-10-12T04:31:49.209345Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# audio = Audio.from_file(\"audios/georg_country.mp3\", n_channels=2).get_segment(to_s=5.01)\n",
"audio = Audio.from_file(\"audios/Erlkoning_tony.mp3\", n_channels=2) #.get_segment(to_s=5.01)\n",
"# audio = load_audio(\"tmp/halo.wav\").get_segment(from_s=49.99, to_s=70)\n",
"audio.play()\n",
"in_artist_arr = process_audio(audio, cfg)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2024-10-12T04:31:56.734909Z",
"iopub.status.busy": "2024-10-12T04:31:56.734743Z",
"iopub.status.idle": "2024-10-12T04:32:20.711043Z",
"shell.execute_reply": "2024-10-12T04:32:20.710377Z",
"shell.execute_reply.started": "2024-10-12T04:31:56.734892Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"