Create app.py

app.py

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+import numpy as np
+import torch
+import gradio as gr
+import spaces
+from queue import Queue
+from threading import Thread
+from typing import Optional
+from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed
+from transformers.generation.streamers import BaseStreamer
+
+model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
+processor = MusicgenProcessor.from_pretrained("facebook/musicgen-small")
+
+title = "9🌍MusicHub - Text to Music Stream Generator"
+description = """  Facebook MusicGen-Small Model - Generate and stream music with model https://huggingface.co/facebook/musicgen-small  """
+article = """
+## How It Works:
+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.
+"""
+
+
+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 = [
+    #    ["Country acoustic guitar fast line dance singer like  Kenny Chesney and Garth brooks and Luke Combs and Chris Stapleton. bpm: 100", 30, 0.5, 5],
+    #    ["Electronic Dance track with pulsating bass and high energy synths. bpm: 126", 30, 0.5, 5],
+    #    ["Rap Beats with deep bass and snappy snares. bpm: 80", 30, 0.5, 5],
+    #    ["Lo-Fi track with smooth beats and chill vibes. bpm: 100", 30, 0.5, 5],
+    #    ["Global Groove track with international instruments and dance rhythms. bpm: 128", 30, 0.5, 5],
+    #    ["Relaxing Meditation music with ambient pads and soothing melodies. bpm: 80", 30, 0.5, 5],
+    #    ["Rave Dance track with hard-hitting beats and euphoric synths. bpm: 128", 30, 0.5, 5]
+    #],
+
+    examples = [
+    ["🌟 Dance/EDM track with vibrant synths and a driving beat. bpm: 128", 30, 0.5, 5],
+    ["💼 Corporate theme with an upbeat tempo and motivational melodies. bpm: 120", 30, 0.5, 5],
+    ["🎸 Rock anthem with powerful guitar riffs and energetic drums. bpm: 140", 30, 0.5, 5],
+    ["🌊 Chill Out track with soothing ambient sounds and relaxed tempo. bpm: 90", 30, 0.5, 5],
+    ["🎤 Hip Hop beat with hard-hitting bass and catchy rhythms. bpm: 95", 30, 0.5, 5],
+    ["🎻 Orchestral piece with dramatic strings and grand composition. bpm: 70", 30, 0.5, 5],
+    ["🕺 Funk groove with groovy basslines and rhythmic guitars. bpm: 110", 30, 0.5, 5],
+    ["🕹️ Video Game music with retro synths and catchy chiptune melodies. bpm: 130", 30, 0.5, 5],
+    ["🌾 Folk song with acoustic guitar and harmonious vocals. bpm: 85", 30, 0.5, 5],
+    ["🌙 Ambient soundscape with ethereal pads and calming tones. bpm: 60", 30, 0.5, 5],
+    ["🎷 Jazz tune with smooth saxophone and swinging rhythms. bpm: 120", 30, 0.5, 5],
+    ["👶 Kids music with cheerful melodies and playful instruments. bpm: 100", 30, 0.5, 5],
+    ["🌟 Pop hit with catchy hooks and upbeat rhythms. bpm: 115", 30, 0.5, 5],
+    ["🎬 Production music with versatile sounds for various media. bpm: 110", 30, 0.5, 5],
+    ["🔊 Electronic piece with experimental sounds and unique textures. bpm: 125", 30, 0.5, 5],
+    ["🌴 Reggae rhythm with laid-back vibes and offbeat guitar chords. bpm: 75", 30, 0.5, 5],
+    ["💃 Dance track with infectious beats and lively energy. bpm: 130", 30, 0.5, 5],
+    ["🎤 R&B tune with smooth vocals and soulful grooves. bpm: 90", 30, 0.5, 5],
+    ["🎉 Latin song with rhythmic percussion and fiery melodies. bpm: 105", 30, 0.5, 5],
+    ["🤠 Country track with twangy guitars and heartfelt storytelling. bpm: 85", 30, 0.5, 5],
+    ["🎶 Indian music with traditional instruments and intricate rhythms. bpm: 95", 30, 0.5, 5]
+    ],
+
+
+    title=title,
+    description=description,
+    article=article,
+    cache_examples=False,
+)
+
+demo.queue().launch()