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
+
+
+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)
+
+    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:
+        yield gr.make_waveform((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", format="numpy", streaming=True, autoplay=True)
+    ],
+)
+
+demo.queue().launch()