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
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
processor = MusicgenProcessor.from_pretrained("facebook/musicgen-small")
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
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
@spaces.GPU
def generate_audio(text_prompt, audio_length_in_s=10.0, play_steps_in_s=2.0):
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.half();
inputs = processor(
text=text_prompt,
padding=True,
return_tensors="pt",
)
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()