"""Basic audio reconstruction experiment.""" import os from os.path import join as p_join import subprocess from datasets import load_dataset import torch from audiocraft.data.audio import audio_read, audio_write from multibanddiffusion import MultiBandDiffusion # configure experiment cache_dir = "audio" os.makedirs(cache_dir, exist_ok=True) def test_files(mbd_model, num_codebooks: int = 8, skip_enhancer: bool = False): """Test with audio files.""" # fetch audio output_audio_dir = p_join(cache_dir, "sample_audio", "original") os.makedirs(output_audio_dir, exist_ok=True) sample_audio_urls = { "common_voice_8_0": "https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0/resolve/main/sample.flac", "jsut_basic5000": "https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac", "reazonspeech_test": "https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test/resolve/main/sample.flac" } for file, url in sample_audio_urls.items(): subprocess.run(["wget", url, "-O", p_join(output_audio_dir, f"{file}.sample.flac")]) # reconstruct audio output_reconstructed_dir = p_join(cache_dir, "sample_audio", f"reconstructed_{num_codebooks}codes") os.makedirs(output_reconstructed_dir, exist_ok=True) for file in sample_audio_urls.keys(): # read audio from file single_file = p_join(output_audio_dir, f"{file}.sample.flac") wav, sr = audio_read(single_file) wav = wav.unsqueeze(0) # tokenize audio tokens = mbd_model.wav_to_tokens(wav, sr) # de-tokenize token re_wav, sr = mbd_model.tokens_to_wav(tokens, skip_enhancer=skip_enhancer) # save the reconstructed wav if skip_enhancer: output = p_join(output_reconstructed_dir, f"{file}.sample") else: output = p_join(f"{output_reconstructed_dir}.enhancer", f"{file}.sample") audio_write(output, re_wav[0], sr, strategy="loudness", loudness_compressor=True) def test_hf(mbd_model, hf_dataset: str, num_codebooks: int = 8, sample_size: int = 128, batch_size: int = 32, skip_enhancer: bool = False): """Test with huggingface audio dataset.""" output_dir = p_join(cache_dir, os.path.basename(hf_dataset)) os.makedirs(output_dir, exist_ok=True) dataset = load_dataset(hf_dataset, split="test") dataset = dataset.select(range(sample_size)) dataset = dataset.map( lambda batch: {k: [v] for k, v in batch.items()}, batched=True, batch_size=batch_size ) for data in dataset: # get sampling rate (all wav must be the same sampling rate) sr_list = [d["sampling_rate"] for d in data["audio"]] assert len(set(sr_list)) == 1, sr_list sr = sr_list[0] # get wav array (batch, channel, time) array = [d["array"] for d in data["audio"]] max_length = max([len(a) for a in array]) array = [a + [0] * (max_length - len(a)) for a in array] wav = torch.as_tensor(array, dtype=torch.float32).unsqueeze_(1) # save the original wav for idx, one_wav in enumerate(wav): output = p_join(output_dir, "original", str(idx)) audio_write(output, one_wav, sr, strategy="loudness", loudness_compressor=True) # tokenize audio tokens = mbd_model.wav_to_tokens(wav, sr) # de-tokenize token re_wav, sr = mbd_model.tokens_to_wav(tokens, skip_enhancer=skip_enhancer) # save the reconstructed wav for idx, one_wav in enumerate(re_wav): if skip_enhancer: output = p_join(output_dir, f"reconstructed_{num_codebooks}codes", str(idx)) else: output = p_join(output_dir, f"reconstructed_{num_codebooks}codes.enhancer", str(idx)) audio_write(output, one_wav, sr, strategy="loudness", loudness_compressor=True) if __name__ == '__main__': # without enhancer for n_code in [2, 3, 4, 5, 6]: model = MultiBandDiffusion.from_pretrained(num_codebooks_decoder=n_code, num_codebooks_encoder=n_code) test_files(model, n_code, skip_enhancer=True) test_hf(model, "japanese-asr/ja_asr.reazonspeech_test", num_codebooks=n_code, sample_size=64, batch_size=16, skip_enhancer=True) test_hf(model, "japanese-asr/ja_asr.jsut_basic5000", num_codebooks=n_code, sample_size=64, batch_size=16, skip_enhancer=True) # with enhancer n_code = 3 model = MultiBandDiffusion.from_pretrained(num_codebooks_decoder=n_code, num_codebooks_encoder=n_code) test_files(model, n_code) test_hf(model, "japanese-asr/ja_asr.reazonspeech_test", num_codebooks=n_code, sample_size=64, batch_size=16) test_hf(model, "japanese-asr/ja_asr.jsut_basic5000", num_codebooks=n_code, sample_size=64, batch_size=16)