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mustango.py
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mustango.py
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import json
import torch
import numpy as np
from huggingface_hub import snapshot_download
from audioldm.audio.stft import TacotronSTFT
from audioldm.variational_autoencoder import AutoencoderKL
from transformers import AutoTokenizer, T5ForConditionalGeneration
from modelling_deberta_v2 import DebertaV2ForTokenClassificationRegression
from diffusers import DDPMScheduler
from models import MusicAudioDiffusion
class MusicFeaturePredictor:
def __init__(self, path, device="cuda:0", cache_dir=None, local_files_only=False):
self.beats_tokenizer = AutoTokenizer.from_pretrained(
"microsoft/deberta-v3-large",
cache_dir=cache_dir,
local_files_only=local_files_only,
)
self.beats_model = DebertaV2ForTokenClassificationRegression.from_pretrained(
"microsoft/deberta-v3-large",
cache_dir=cache_dir,
local_files_only=local_files_only,
)
self.beats_model.eval()
self.beats_model.to(device)
beats_ckpt = f"{path}/beats/microsoft-deberta-v3-large.pt"
beats_weight = torch.load(beats_ckpt, map_location="cpu")
self.beats_model.load_state_dict(beats_weight)
self.chords_tokenizer = AutoTokenizer.from_pretrained(
"google/flan-t5-large",
cache_dir=cache_dir,
local_files_only=local_files_only,
)
self.chords_model = T5ForConditionalGeneration.from_pretrained(
"google/flan-t5-large",
cache_dir=cache_dir,
local_files_only=local_files_only,
)
self.chords_model.eval()
self.chords_model.to(device)
chords_ckpt = f"{path}/chords/flan-t5-large.bin"
chords_weight = torch.load(chords_ckpt, map_location="cpu")
self.chords_model.load_state_dict(chords_weight)
def generate_beats(self, prompt):
tokenized = self.beats_tokenizer(
prompt, max_length=512, padding=True, truncation=True, return_tensors="pt"
)
tokenized = {k: v.to(self.beats_model.device) for k, v in tokenized.items()}
with torch.no_grad():
out = self.beats_model(**tokenized)
max_beat = (
1 + torch.argmax(out["logits"][:, 0, :], -1).detach().cpu().numpy()
).tolist()[0]
intervals = (
out["values"][:, :, 0]
.detach()
.cpu()
.numpy()
.astype("float32")
.round(4)
.tolist()
)
intervals = np.cumsum(intervals)
predicted_beats_times = []
for t in intervals:
if t < 10:
predicted_beats_times.append(round(t, 2))
else:
break
predicted_beats_times = list(np.array(predicted_beats_times)[:50])
if len(predicted_beats_times) == 0:
predicted_beats = [[], []]
else:
beat_counts = []
for i in range(len(predicted_beats_times)):
beat_counts.append(float(1.0 + np.mod(i, max_beat)))
predicted_beats = [[predicted_beats_times, beat_counts]]
return max_beat, predicted_beats_times, predicted_beats
def generate(self, prompt):
max_beat, predicted_beats_times, predicted_beats = self.generate_beats(prompt)
chords_prompt = "Caption: {} \\n Timestamps: {} \\n Max Beat: {}".format(
prompt,
" , ".join([str(round(t, 2)) for t in predicted_beats_times]),
max_beat,
)
tokenized = self.chords_tokenizer(
chords_prompt,
max_length=512,
padding=True,
truncation=True,
return_tensors="pt",
)
tokenized = {k: v.to(self.chords_model.device) for k, v in tokenized.items()}
generated_chords = self.chords_model.generate(
input_ids=tokenized["input_ids"],
attention_mask=tokenized["attention_mask"],
min_length=8,
max_length=128,
num_beams=5,
early_stopping=True,
num_return_sequences=1,
)
generated_chords = self.chords_tokenizer.decode(
generated_chords[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
).split(" n ")
predicted_chords, predicted_chords_times = [], []
for item in generated_chords:
c, ct = item.split(" at ")
predicted_chords.append(c)
predicted_chords_times.append(float(ct))
return predicted_beats, predicted_chords, predicted_chords_times
class Mustango:
def __init__(
self,
name="declare-lab/mustango",
device="cuda:0",
cache_dir=None,
local_files_only=False,
):
path = snapshot_download(repo_id=name, cache_dir=cache_dir)
self.music_model = MusicFeaturePredictor(
path, device, cache_dir=cache_dir, local_files_only=local_files_only
)
vae_config = json.load(open(f"{path}/configs/vae_config.json"))
stft_config = json.load(open(f"{path}/configs/stft_config.json"))
main_config = json.load(open(f"{path}/configs/main_config.json"))
self.vae = AutoencoderKL(**vae_config).to(device)
self.stft = TacotronSTFT(**stft_config).to(device)
self.model = MusicAudioDiffusion(
main_config["text_encoder_name"],
main_config["scheduler_name"],
unet_model_config_path=f"{path}/configs/music_diffusion_model_config.json",
).to(device)
vae_weights = torch.load(
f"{path}/vae/pytorch_model_vae.bin", map_location=device
)
stft_weights = torch.load(
f"{path}/stft/pytorch_model_stft.bin", map_location=device
)
main_weights = torch.load(
f"{path}/ldm/pytorch_model_ldm.bin", map_location=device
)
self.vae.load_state_dict(vae_weights)
self.stft.load_state_dict(stft_weights)
self.model.load_state_dict(main_weights)
print("Successfully loaded checkpoint from:", name)
self.vae.eval()
self.stft.eval()
self.model.eval()
self.scheduler = DDPMScheduler.from_pretrained(
main_config["scheduler_name"], subfolder="scheduler"
)
def generate(self, prompt, steps=100, guidance=3, samples=1, disable_progress=True):
"""Genrate music for a single prompt string."""
with torch.no_grad():
beats, chords, chords_times = self.music_model.generate(prompt)
latents = self.model.inference(
[prompt],
beats,
[chords],
[chords_times],
self.scheduler,
steps,
guidance,
samples,
disable_progress,
)
mel = self.vae.decode_first_stage(latents)
wave = self.vae.decode_to_waveform(mel)
return wave[0]