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Running
on
Zero
from queue import Queue | |
from threading import Thread | |
from typing import Optional | |
import numpy as np | |
import torch | |
from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed | |
from transformers.generation.streamers import BaseStreamer | |
import gradio as gr | |
import spaces | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") | |
processor = MusicgenProcessor.from_pretrained("facebook/musicgen-small") | |
if device == "cuda:0": | |
model.to(device).half(); | |
class MusicgenStreamer(BaseStreamer): | |
def __init__( | |
self, | |
model: MusicgenForConditionalGeneration, | |
device: Optional[str] = None, | |
play_steps: Optional[int] = 10, | |
stride: Optional[int] = None, | |
timeout: Optional[float] = None, | |
): | |
""" | |
Streamer that stores playback-ready audio in a queue, to be used by a downstream application as an iterator. This is | |
useful for applications that benefit from acessing the generated audio in a non-blocking way (e.g. in an interactive | |
Gradio demo). | |
Parameters: | |
model (`MusicgenForConditionalGeneration`): | |
The MusicGen model used to generate the audio waveform. | |
device (`str`, *optional*): | |
The torch device on which to run the computation. If `None`, will default to the device of the model. | |
play_steps (`int`, *optional*, defaults to 10): | |
The number of generation steps with which to return the generated audio array. Using fewer steps will | |
mean the first chunk is ready faster, but will require more codec decoding steps overall. This value | |
should be tuned to your device and latency requirements. | |
stride (`int`, *optional*): | |
The window (stride) between adjacent audio samples. Using a stride between adjacent audio samples reduces | |
the hard boundary between them, giving smoother playback. If `None`, will default to a value equivalent to | |
play_steps // 6 in the audio space. | |
timeout (`int`, *optional*): | |
The timeout for the audio queue. If `None`, the queue will block indefinitely. Useful to handle exceptions | |
in `.generate()`, when it is called in a separate thread. | |
""" | |
self.decoder = model.decoder | |
self.audio_encoder = model.audio_encoder | |
self.generation_config = model.generation_config | |
self.device = device if device is not None else model.device | |
# variables used in the streaming process | |
self.play_steps = play_steps | |
if stride is not None: | |
self.stride = stride | |
else: | |
hop_length = np.prod(self.audio_encoder.config.upsampling_ratios) | |
self.stride = hop_length * (play_steps - self.decoder.num_codebooks) // 6 | |
self.token_cache = None | |
self.to_yield = 0 | |
# varibles used in the thread process | |
self.audio_queue = Queue() | |
self.stop_signal = None | |
self.timeout = timeout | |
def apply_delay_pattern_mask(self, input_ids): | |
# build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen) | |
_, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask( | |
input_ids[:, :1], | |
pad_token_id=self.generation_config.decoder_start_token_id, | |
max_length=input_ids.shape[-1], | |
) | |
# apply the pattern mask to the input ids | |
input_ids = self.decoder.apply_delay_pattern_mask(input_ids, decoder_delay_pattern_mask) | |
# revert the pattern delay mask by filtering the pad token id | |
input_ids = input_ids[input_ids != self.generation_config.pad_token_id].reshape( | |
1, self.decoder.num_codebooks, -1 | |
) | |
# append the frame dimension back to the audio codes | |
input_ids = input_ids[None, ...] | |
# send the input_ids to the correct device | |
input_ids = input_ids.to(self.audio_encoder.device) | |
output_values = self.audio_encoder.decode( | |
input_ids, | |
audio_scales=[None], | |
) | |
audio_values = output_values.audio_values[0, 0] | |
return audio_values.cpu().float().numpy() | |
def put(self, value): | |
batch_size = value.shape[0] // self.decoder.num_codebooks | |
if batch_size > 1: | |
raise ValueError("MusicgenStreamer only supports batch size 1") | |
if self.token_cache is None: | |
self.token_cache = value | |
else: | |
self.token_cache = torch.concatenate([self.token_cache, value[:, None]], dim=-1) | |
if self.token_cache.shape[-1] % self.play_steps == 0: | |
audio_values = self.apply_delay_pattern_mask(self.token_cache) | |
self.on_finalized_audio(audio_values[self.to_yield : -self.stride]) | |
self.to_yield += len(audio_values) - self.to_yield - self.stride | |
def end(self): | |
"""Flushes any remaining cache and appends the stop symbol.""" | |
if self.token_cache is not None: | |
audio_values = self.apply_delay_pattern_mask(self.token_cache) | |
else: | |
audio_values = np.zeros(self.to_yield) | |
self.on_finalized_audio(audio_values[self.to_yield :], stream_end=True) | |
def on_finalized_audio(self, audio: np.ndarray, stream_end: bool = False): | |
"""Put the new audio in the queue. If the stream is ending, also put a stop signal in the queue.""" | |
self.audio_queue.put(audio, timeout=self.timeout) | |
if stream_end: | |
self.audio_queue.put(self.stop_signal, timeout=self.timeout) | |
def __iter__(self): | |
return self | |
def __next__(self): | |
value = self.audio_queue.get(timeout=self.timeout) | |
if not isinstance(value, np.ndarray) and value == self.stop_signal: | |
raise StopIteration() | |
else: | |
return value | |
sampling_rate = model.audio_encoder.config.sampling_rate | |
frame_rate = model.audio_encoder.config.frame_rate | |
def generate_audio(text_prompt, audio_length_in_s=10.0, play_steps_in_s=2.0): | |
inputs = processor( | |
text=text_prompt, | |
padding=True, | |
return_tensors="pt", | |
) | |
max_new_tokens = int(frame_rate * audio_length_in_s) | |
play_steps = int(frame_rate * play_steps_in_s) | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
if device != model.device: | |
model.to(device) | |
if device == "cuda:0": | |
model.to(device).half(); | |
streamer = MusicgenStreamer(model, device=device, play_steps=play_steps) | |
generation_kwargs = dict( | |
**inputs.to(device), | |
streamer=streamer, | |
max_new_tokens=max_new_tokens, | |
) | |
thread = Thread(target=model.generate, kwargs=generation_kwargs) | |
thread.start() | |
set_seed(0) | |
for new_audio in streamer: | |
print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds") | |
new_audio = (new_audio * max_range).astype(np.int16) | |
yield (sampling_rate, new_audio) | |
demo = gr.Interface( | |
fn=generate_audio, | |
inputs=[ | |
gr.Text(label="Prompt", value="80s pop track with synth and instrumentals"), | |
gr.Slider(10, 30, value=15, step=5, label="Audio length in s"), | |
gr.Slider(2, 10, value=2, step=2, label="Streaming interval in s"), | |
], | |
outputs=[ | |
gr.Audio(label="Generated Music", streaming=True, autoplay=True) | |
], | |
) | |
demo.queue().launch() |