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| 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") | |
| title = "MusicGen Streaming" | |
| description = """ | |
| Stream the outputs of the MusicGen text-to-music model by playing the generated audio as soon as the first chunk is ready. | |
| Demo uses [MusicGen Small](https://huggingface.co/facebook/musicgen-small) in the 🤗 Transformers library. Note that the | |
| demo works best on the Chrome browser. If there is no audio output, try switching browser to Chrome. | |
| """ | |
| article = """ | |
| ## How Does It Work? | |
| MusicGen is an auto-regressive transformer-based model, meaning generates audio codes (tokens) in a causal fashion. | |
| At each decoding step, the model generates a new set of audio codes, conditional on the text input and all previous audio codes. From the | |
| frame rate of the [EnCodec model](https://huggingface.co/facebook/encodec_32khz) used to decode the generated codes to audio waveform, | |
| each set of generated audio codes corresponds to 0.02 seconds. This means we require a total of 1000 decoding steps to generate | |
| 20 seconds of audio. | |
| Rather than waiting for the entire audio sequence to be generated, which would require the full 1000 decoding steps, we can start | |
| playing the audio after a specified number of decoding steps have been reached, a techinque known as [*streaming*](https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming). | |
| For example, after 250 steps we have the first 5 seconds of audio ready, and so can play this without waiting for the remaining | |
| 750 decoding steps to be complete. As we continue to generate with the MusicGen model, we append new chunks of generated audio | |
| to our output waveform on-the-fly. After the full 1000 decoding steps, the generated audio is complete, and is composed of four | |
| chunks of audio, each corresponding to 250 tokens. | |
| This method of playing incremental generations reduces the latency of the MusicGen model from the total time to generate 1000 tokens, | |
| to the time taken to play the first chunk of audio (250 tokens). This can result in significant improvements to perceived latency, | |
| particularly when the chunk size is chosen to be small. In practice, the chunk size should be tuned to your device: using a | |
| smaller chunk size will mean that the first chunk is ready faster, but should not be chosen so small that the model generates slower | |
| than the time it takes to play the audio. | |
| For details on how the streaming class works, check out the source code for the [MusicgenStreamer](https://huggingface.co/spaces/sanchit-gandhi/musicgen-streaming/blob/main/app.py#L52). | |
| """ | |
| 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 accessing 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 | |
| def generate_audio(text_prompt, audio_length_in_s=10.0, play_steps_in_s=2.0, seed=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(seed) | |
| 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 seconds"), | |
| gr.Slider(0.5, 2.5, value=1.5, step=0.5, label="Streaming interval in seconds", info="Lower = shorter chunks, lower latency, more codec steps"), | |
| gr.Slider(0, 10, value=5, step=1, label="Seed for random generations"), | |
| ], | |
| outputs=[ | |
| gr.Audio(label="Generated Music", streaming=True, autoplay=True) | |
| ], | |
| examples=[ | |
| ["An 80s driving pop song with heavy drums and synth pads in the background", 30, 1.5, 5], | |
| ["A cheerful country song with acoustic guitars", 30, 1.5, 5], | |
| ["90s rock song with electric guitar and heavy drums", 30, 1.5, 5], | |
| ["a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130", 30, 1.5, 5], | |
| ["lofi slow bpm electro chill with organic samples", 30, 1.5, 5], | |
| ], | |
| title=title, | |
| description=description, | |
| article=article, | |
| cache_examples=False, | |
| ) | |
| demo.queue().launch() |