audiocraft/audiogen.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""
Main model for using AudioGen. This will combine all the required components
and provide easy access to the generation API.
"""

import typing as tp
import torch

from audiocraft.encodec import CompressionModel
from audiocraft.genmodel import BaseGenModel
from audiocraft.lm import LMModel
from audiocraft.loaders import load_compression_model, load_lm_model
from .utils.audio_utils import f32_pcm, normalize_audio


def audio_write(stem_name,
                wav, 
                sample_rate,
                format= 'wav', 
                mp3_rate=320, 
                ogg_rate= None,
                normalize= True,
                strategy= 'peak',
                peak_clip_headroom_db=1,
                rms_headroom_db= 18, 
                loudness_headroom_db = 14,
                loudness_compressor = False,
                log_clipping = True,
                make_parent_dir = True,
                add_suffix = True):
    
    assert wav.dtype.is_floating_point, "wav is not floating point"
    if wav.dim() == 1:
        wav = wav[None]
    elif wav.dim() > 2:
        raise ValueError("Input wav should be at most 2 dimension.")
    assert wav.isfinite().all()
    wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db,
                          rms_headroom_db, loudness_headroom_db, loudness_compressor,
                          log_clipping=log_clipping, sample_rate=sample_rate,
                          stem_name=str(stem_name))
    return wav
# ===

class AudioGen(BaseGenModel):
    """AudioGen main model with convenient generation API.

    Args:
        name (str): name of the model.
        compression_model (CompressionModel): Compression model
            used to map audio to invertible discrete representations.
        lm (LMModel): Language model over discrete representations.
        max_duration (float, optional): maximum duration the model can produce,
            otherwise, inferred from the training params.
    """
    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
                 max_duration: tp.Optional[float] = None):
        # print(f'Using {compression_model=}\n-----=-----')
        super().__init__(name, compression_model, lm, max_duration)
        self.set_generation_params(duration=5)  # default duration

    @staticmethod
    def get_pretrained(name: str = 'facebook/audiogen-medium', device=None):
        """Return pretrained model, we provide a single model for now:
        - facebook/audiogen-medium (1.5B), text to sound,
          # see: https://huggingface.co/facebook/audiogen-medium
        """
        if device is None:
            if torch.cuda.device_count():
                device = 'cuda'
            else:
                device = 'cpu'

        

        compression_model = load_compression_model(name, device=device)
        lm = load_lm_model(name, device=device)
        assert 'self_wav' not in lm.condition_provider.conditioners, \
            "AudioGen do not support waveform conditioning for now"
        return AudioGen(name, compression_model, lm)

    def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
                              top_p: float = 0.0, temperature: float = 1.0,
                              duration: float = 10.0, cfg_coef: float = 3.0,
                              two_step_cfg: bool = False, extend_stride: float = 2):
        """Set the generation parameters for AudioGen.

        Args:
            use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
            top_k (int, optional): top_k used for sampling. Defaults to 250.
            top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
            temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
            duration (float, optional): Duration of the generated waveform. Defaults to 10.0.
            cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
            two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
                instead of batching together the two. This has some impact on how things
                are padded but seems to have little impact in practice.
            extend_stride: when doing extended generation (i.e. more than 10 seconds), by how much
                should we extend the audio each time. Larger values will mean less context is
                preserved, and shorter value will require extra computations.
        """
        assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
        self.extend_stride = extend_stride
        self.duration = duration
        self.generation_params = {
            'use_sampling': use_sampling,
            'temp': temperature,
            'top_k': top_k,
            'top_p': top_p,
            'cfg_coef': cfg_coef,
            'two_step_cfg': two_step_cfg,
        }