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import torch
import torchaudio
import numpy as np
import re
from hyperpyyaml import load_hyperpyyaml
import uuid
from collections import defaultdict
def fade_in_out(fade_in_mel, fade_out_mel, window):
device = fade_in_mel.device
fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
mel_overlap_len = int(window.shape[0] / 2)
fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \
fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
return fade_in_mel.to(device)
class AudioDecoder:
def __init__(self, config_path, flow_ckpt_path, hift_ckpt_path, device="cuda"):
self.device = device
with open(config_path, 'r') as f:
self.scratch_configs = load_hyperpyyaml(f)
# Load models
self.flow = self.scratch_configs['flow']
self.flow.load_state_dict(torch.load(flow_ckpt_path, map_location=self.device))
self.hift = self.scratch_configs['hift']
self.hift.load_state_dict(torch.load(hift_ckpt_path, map_location=self.device))
# Move models to the appropriate device
self.flow.to(self.device)
self.hift.to(self.device)
self.mel_overlap_dict = defaultdict(lambda: None)
self.hift_cache_dict = defaultdict(lambda: None)
self.token_min_hop_len = 2 * self.flow.input_frame_rate
self.token_max_hop_len = 4 * self.flow.input_frame_rate
self.token_overlap_len = 5
self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
self.mel_window = np.hamming(2 * self.mel_overlap_len)
# hift cache
self.mel_cache_len = 1
self.source_cache_len = int(self.mel_cache_len * 256)
# speech fade in out
self.speech_window = np.hamming(2 * self.source_cache_len)
def token2wav(self, token, uuid, prompt_token=torch.zeros(1, 0, dtype=torch.int32),
prompt_feat=torch.zeros(1, 0, 80), embedding=torch.zeros(1, 192), finalize=False):
tts_mel = self.flow.inference(token=token.to(self.device),
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(
self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(
self.device),
embedding=embedding.to(self.device))
# mel overlap fade in out
if self.mel_overlap_dict[uuid] is not None:
tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
# append hift cache
if self.hift_cache_dict[uuid] is not None:
hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
else:
hift_cache_source = torch.zeros(1, 1, 0)
# _tts_mel=tts_mel.contiguous()
# keep overlap mel and hift cache
if finalize is False:
self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
# if self.hift_cache_dict[uuid] is not None:
# tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
tts_speech = tts_speech[:, :-self.source_cache_len]
else:
tts_speech, tts_source = self.hift.inference(mel=tts_mel, cache_source=hift_cache_source)
del self.hift_cache_dict[uuid]
del self.mel_overlap_dict[uuid]
# if uuid in self.hift_cache_dict.keys() and self.hift_cache_dict[uuid] is not None:
# tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
return tts_speech, tts_mel
def offline_inference(self, token):
this_uuid = str(uuid.uuid1())
tts_speech, tts_mel = self.token2wav(token, uuid=this_uuid, finalize=True)
return tts_speech.cpu()
def stream_inference(self, token):
token.to(self.device)
this_uuid = str(uuid.uuid1())
# Prepare other necessary input tensors
llm_embedding = torch.zeros(1, 192).to(self.device)
prompt_speech_feat = torch.zeros(1, 0, 80).to(self.device)
flow_prompt_speech_token = torch.zeros(1, 0, dtype=torch.int32).to(self.device)
tts_speechs = []
tts_mels = []
block_size = self.flow.encoder.block_size
prev_mel = None
for idx in range(0, token.size(1), block_size):
# if idx>block_size: break
tts_token = token[:, idx:idx + block_size]
print(tts_token.size())
if prev_mel is not None:
prompt_speech_feat = torch.cat(tts_mels, dim=-1).transpose(1, 2)
flow_prompt_speech_token = token[:, :idx]
if idx + block_size >= token.size(-1):
is_finalize = True
else:
is_finalize = False
tts_speech, tts_mel = self.token2wav(tts_token, uuid=this_uuid,
prompt_token=flow_prompt_speech_token.to(self.device),
prompt_feat=prompt_speech_feat.to(self.device), finalize=is_finalize)
prev_mel = tts_mel
prev_speech = tts_speech
print(tts_mel.size())
tts_speechs.append(tts_speech)
tts_mels.append(tts_mel)
# Convert Mel spectrogram to audio using HiFi-GAN
tts_speech = torch.cat(tts_speechs, dim=-1).cpu()
return tts_speech.cpu()
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