Spaces:
Runtime error
Runtime error
File size: 5,146 Bytes
5548515 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import os
import pickle
from tqdm import tqdm
import numpy as np
from modules import whisper_extractor as whisper
def whisper_encoder_batch(model, audio_paths):
batch = len(audio_paths)
batch_mel = torch.zeros((batch, 80, 3000), dtype=torch.float32, device=model.device)
for i, audio_path in enumerate(audio_paths):
# (48000,)
audio = whisper.load_audio(str(audio_path))
audio = whisper.pad_or_trim(audio)
# (80, 3000)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
batch_mel[i] = mel
with torch.no_grad():
# (batch, 1500, 1024)
features = model.embed_audio(batch_mel)
return features.cpu().detach().numpy()
def whisper_encoder(model, audio_path):
audio = whisper.load_audio(str(audio_path))
audio = whisper.pad_or_trim(audio)
# (80, 3000)
mel = whisper.log_mel_spectrogram(audio).to(model.device).unsqueeze(0)
with torch.no_grad():
# (1, 1500, 1024) -> # (1500, 1024)
features = model.embed_audio(mel).squeeze(0)
return features.cpu().detach().numpy()
def get_mapped_whisper_features(
raw_whisper_features, mapping_features, fast_mapping=True
):
"""
Whisper: frameshift = 20ms (30s audio -> 1500 frames), hop_size = 480 in 24k
# Ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/model.py#L136
Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms)
"""
source_hop = 480
target_hop = 256
factor = np.gcd(source_hop, target_hop)
source_hop //= factor
target_hop //= factor
print(
"Mapping source's {} frames => target's {} frames".format(
target_hop, source_hop
)
)
max_source_len = 1500
whisper_features = []
for index, mapping_feat in enumerate(tqdm(mapping_features)):
# mapping_feat: (mels_frame_len, n_mels)
target_len = mapping_feat.shape[0]
# The max target_len is 2812
target_len = min(target_len, max_source_len * source_hop // target_hop)
# (1500, dim)
raw_feats = raw_whisper_features[index]
width = raw_feats.shape[-1]
if fast_mapping:
source_len = target_len * target_hop // source_hop + 1
raw_feats = raw_feats[:source_len]
else:
source_len = max_source_len
# const ~= target_len * target_hop
const = source_len * source_hop // target_hop * target_hop
# (source_len * source_hop, dim)
up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0)
# (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
down_sampling_feats = np.average(
up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
)
assert len(down_sampling_feats) >= target_len
# (target_len, dim)
feats = down_sampling_feats[:target_len]
whisper_features.append(feats)
return whisper_features
def load_whisper_model(hps):
print("Loading Whisper Model: ", hps.whisper_model)
model = whisper.load_model(hps.whisper_model)
if torch.cuda.is_available():
model = model.cuda()
model = model.eval()
return model
def load_target_acoustic_features(
output_path, dataset, acoustic_features_name, acoustic_features_fs, dataset_type
):
mapping_dir = os.path.join(
output_path,
dataset,
"{}/{}".format(acoustic_features_name, acoustic_features_fs),
)
with open(os.path.join(mapping_dir, "{}.pkl".format(dataset_type)), "rb") as f:
mapping_features = pickle.load(f)
# Mels: (n_mels, frame_len) -> (frame_len, n_mels)
if acoustic_features_name == "mels":
print("Transposing mel features...")
mapping_features = [feat.T for feat in mapping_features]
print(
"Mapping to the acoustic features {}, #sz = {}, feats[0] is {}".format(
acoustic_features_name, len(mapping_features), mapping_features[0].shape
)
)
return mapping_features
def extract_whisper_features_of_dataset(
datasets,
model,
batch_size,
out_dir,
):
audio_paths = [utt["Path"] for utt in datasets]
if len(audio_paths) < batch_size:
batch_size = len(audio_paths)
start, end = 0, 0
while end < len(audio_paths):
# Raw features: (batch_size, 1500, dim)
start = end
end = start + batch_size
tmp_raw_whisper_features = whisper_encoder_batch(model, audio_paths[start:end])
# Mapping to acoustic features' lengths
for index, utt in enumerate(tqdm(datasets[start:end])):
uid = utt["Uid"]
raw_whisper_feature = tmp_raw_whisper_features[index]
save_path = os.path.join(out_dir, uid + ".npy")
np.save(save_path, raw_whisper_feature)
print("{}/{} Done...".format(end, len(audio_paths)))
|