Create app.py

app.py

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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 browsers 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 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, 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=0.5, 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", 20, 0.5, 5],
+        ["A cheerful country song with acoustic guitars", 15, 0.5, 5],
+        ["90s rock song with electric guitar and heavy drums", 15, 0.5, 5],
+        ["a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130", 30, 0.5, 5],
+        ["lofi slow bpm electro chill with organic samples", 30, 0.5, 5],
+    ],
+    title=title,
+    description=description,
+    article=article,
+    cache_examples=False,
+)
+
+
+demo.queue().launch()