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# 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)))
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