mmaudio/eval_utils.py

import dataclasses
import logging
from pathlib import Path
from typing import Optional

import torch
from colorlog import ColoredFormatter
from torchvision.transforms import v2
from torio.io import StreamingMediaDecoder, StreamingMediaEncoder
import av

from mmaudio.model.flow_matching import FlowMatching
from mmaudio.model.networks import MMAudio
from mmaudio.model.sequence_config import (CONFIG_16K, CONFIG_44K, SequenceConfig)
from mmaudio.model.utils.features_utils import FeaturesUtils
from mmaudio.utils.download_utils import download_model_if_needed

log = logging.getLogger()


@dataclasses.dataclass
class ModelConfig:
    model_name: str
    model_path: Path
    vae_path: Path
    bigvgan_16k_path: Optional[Path]
    mode: str
    synchformer_ckpt: Path = Path('./ext_weights/synchformer_state_dict.pth')

    @property
    def seq_cfg(self) -> SequenceConfig:
        if self.mode == '16k':
            return CONFIG_16K
        elif self.mode == '44k':
            return CONFIG_44K

    def download_if_needed(self):
        download_model_if_needed(self.model_path)
        download_model_if_needed(self.vae_path)
        if self.bigvgan_16k_path is not None:
            download_model_if_needed(self.bigvgan_16k_path)
        download_model_if_needed(self.synchformer_ckpt)


small_16k = ModelConfig(model_name='small_16k',
                        model_path=Path('./weights/mmaudio_small_16k.pth'),
                        vae_path=Path('./ext_weights/v1-16.pth'),
                        bigvgan_16k_path=Path('./ext_weights/best_netG.pt'),
                        mode='16k')
small_44k = ModelConfig(model_name='small_44k',
                        model_path=Path('./weights/mmaudio_small_44k.pth'),
                        vae_path=Path('./ext_weights/v1-44.pth'),
                        bigvgan_16k_path=None,
                        mode='44k')
medium_44k = ModelConfig(model_name='medium_44k',
                         model_path=Path('./weights/mmaudio_medium_44k.pth'),
                         vae_path=Path('./ext_weights/v1-44.pth'),
                         bigvgan_16k_path=None,
                         mode='44k')
large_44k = ModelConfig(model_name='large_44k',
                        model_path=Path('./weights/mmaudio_large_44k.pth'),
                        vae_path=Path('./ext_weights/v1-44.pth'),
                        bigvgan_16k_path=None,
                        mode='44k')
large_44k_v2 = ModelConfig(model_name='large_44k_v2',
                           model_path=Path('./weights/mmaudio_large_44k_v2.pth'),
                           vae_path=Path('./ext_weights/v1-44.pth'),
                           bigvgan_16k_path=None,
                           mode='44k')
all_model_cfg: dict[str, ModelConfig] = {
    'small_16k': small_16k,
    'small_44k': small_44k,
    'medium_44k': medium_44k,
    'large_44k': large_44k,
    'large_44k_v2': large_44k_v2,
}


def generate(clip_video: Optional[torch.Tensor],
             sync_video: Optional[torch.Tensor],
             text: Optional[list[str]],
             *,
             negative_text: Optional[list[str]] = None,
             feature_utils: FeaturesUtils,
             net: MMAudio,
             fm: FlowMatching,
             rng: torch.Generator,
             cfg_strength: float):
    device = feature_utils.device
    dtype = feature_utils.dtype

    bs = len(text)
    if clip_video is not None:
        clip_video = clip_video.to(device, dtype, non_blocking=True)
        clip_features = feature_utils.encode_video_with_clip(clip_video, batch_size=bs)
    else:
        clip_features = net.get_empty_clip_sequence(bs)

    if sync_video is not None:
        sync_video = sync_video.to(device, dtype, non_blocking=True)
        sync_features = feature_utils.encode_video_with_sync(sync_video, batch_size=bs)
    else:
        sync_features = net.get_empty_sync_sequence(bs)

    if text is not None:
        text_features = feature_utils.encode_text(text)
    else:
        text_features = net.get_empty_string_sequence(bs)

    if negative_text is not None:
        assert len(negative_text) == bs
        negative_text_features = feature_utils.encode_text(negative_text)
    else:
        negative_text_features = net.get_empty_string_sequence(bs)

    x0 = torch.randn(bs,
                     net.latent_seq_len,
                     net.latent_dim,
                     device=device,
                     dtype=dtype,
                     generator=rng)
    preprocessed_conditions = net.preprocess_conditions(clip_features, sync_features, text_features)
    empty_conditions = net.get_empty_conditions(
        bs, negative_text_features=negative_text_features if negative_text is not None else None)

    cfg_ode_wrapper = lambda t, x: net.ode_wrapper(t, x, preprocessed_conditions, empty_conditions,
                                                   cfg_strength)
    x1 = fm.to_data(cfg_ode_wrapper, x0)
    x1 = net.unnormalize(x1)
    spec = feature_utils.decode(x1)
    audio = feature_utils.vocode(spec)
    return audio


LOGFORMAT = "  %(log_color)s%(levelname)-8s%(reset)s | %(log_color)s%(message)s%(reset)s"


def setup_eval_logging(log_level: int = logging.INFO):
    logging.root.setLevel(log_level)
    formatter = ColoredFormatter(LOGFORMAT)
    stream = logging.StreamHandler()
    stream.setLevel(log_level)
    stream.setFormatter(formatter)
    log = logging.getLogger()
    log.setLevel(log_level)
    log.addHandler(stream)


def load_video(video_path: Path, duration_sec: float) -> tuple[torch.Tensor, torch.Tensor, float]:
    _CLIP_SIZE = 384
    _CLIP_FPS = 8.0

    _SYNC_SIZE = 224
    _SYNC_FPS = 25.0

    clip_transform = v2.Compose([
        v2.Resize((_CLIP_SIZE, _CLIP_SIZE), interpolation=v2.InterpolationMode.BICUBIC),
        v2.ToImage(),
        v2.ToDtype(torch.float32, scale=True),
    ])

    sync_transform = v2.Compose([
        v2.Resize(_SYNC_SIZE, interpolation=v2.InterpolationMode.BICUBIC),
        v2.CenterCrop(_SYNC_SIZE),
        v2.ToImage(),
        v2.ToDtype(torch.float32, scale=True),
        v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
    ])

    reader = StreamingMediaDecoder(video_path)
    reader.add_basic_video_stream(
        frames_per_chunk=int(_CLIP_FPS * duration_sec),
        buffer_chunk_size=1,
        frame_rate=_CLIP_FPS,
        format='rgb24',
    )
    reader.add_basic_video_stream(
        frames_per_chunk=int(_SYNC_FPS * duration_sec),
        buffer_chunk_size=1,
        frame_rate=_SYNC_FPS,
        format='rgb24',
    )

    reader.fill_buffer()
    data_chunk = reader.pop_chunks()
    clip_chunk = data_chunk[0]
    sync_chunk = data_chunk[1]
    print('clip', clip_chunk.shape, clip_chunk.dtype, clip_chunk.max())
    print('sync', sync_chunk.shape, sync_chunk.dtype, sync_chunk.max())
    assert clip_chunk is not None
    assert sync_chunk is not None

    for i in range(reader.num_out_streams):
        print(reader.get_out_stream_info(i))

    clip_frames = clip_transform(clip_chunk)
    sync_frames = sync_transform(sync_chunk)

    clip_length_sec = clip_frames.shape[0] / _CLIP_FPS
    sync_length_sec = sync_frames.shape[0] / _SYNC_FPS

    if clip_length_sec < duration_sec:
        log.warning(f'Clip video is too short: {clip_length_sec:.2f} < {duration_sec:.2f}')
        log.warning(f'Truncating to {clip_length_sec:.2f} sec')
        duration_sec = clip_length_sec

    if sync_length_sec < duration_sec:
        log.warning(f'Sync video is too short: {sync_length_sec:.2f} < {duration_sec:.2f}')
        log.warning(f'Truncating to {sync_length_sec:.2f} sec')
        duration_sec = sync_length_sec

    clip_frames = clip_frames[:int(_CLIP_FPS * duration_sec)]
    sync_frames = sync_frames[:int(_SYNC_FPS * duration_sec)]

    return clip_frames, sync_frames, duration_sec


def make_video(video_path: Path, output_path: Path, audio: torch.Tensor, sampling_rate: int,
               duration_sec: float):

    av_video = av.open(video_path)
    frame_rate = av_video.streams.video[0].guessed_rate
    print('av frame rate', frame_rate)

    approx_max_length = int(duration_sec * 60)
    reader = StreamingMediaDecoder(video_path)
    reader.add_basic_video_stream(
        frames_per_chunk=approx_max_length,
        buffer_chunk_size=1,
        format='rgb24',
    )
    reader.fill_buffer()
    video_chunk = reader.pop_chunks()[0]
    print(video_chunk.shape, video_chunk.dtype, video_chunk.max())
    assert video_chunk is not None

    # fps = int(reader.get_out_stream_info(0).frame_rate)
    fps = frame_rate
    for i in range(reader.num_out_streams):
        print(reader.get_out_stream_info(i))
    if fps > 60:
        log.warning(f'This code supports only up to 60 fps, but the video has {fps} fps')
        log.warning(f'Just change the *60 above me')

    h, w = video_chunk.shape[-2:]
    video_chunk = video_chunk[:int(fps * duration_sec)]

    writer = StreamingMediaEncoder(output_path)
    writer.add_audio_stream(
        sample_rate=sampling_rate,
        num_channels=audio.shape[0],
        encoder='aac',  # 'flac' does not work for some reason?
    )
    writer.add_video_stream(frame_rate=fps,
                            width=w,
                            height=h,
                            format='rgb24',
                            encoder='libx264',
                            encoder_format='yuv420p')
    with writer.open():
        writer.write_audio_chunk(0, audio.float().transpose(0, 1))
        writer.write_video_chunk(1, video_chunk)