Newer
Older
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
import ChatTTS
import logging
from baseHandler import BaseHandler
import librosa
import numpy as np
from rich.console import Console
import torch
logging.basicConfig(
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
console = Console()
class ChatTTSHandler(BaseHandler):
def setup(
self,
should_listen,
device="mps",
gen_kwargs={}, # Unused
stream=True,
chunk_size=512,
):
self.should_listen = should_listen
self.device = device
self.model = ChatTTS.Chat()
self.model.load(compile=True) # Set to True for better performance
self.chunk_size = chunk_size
self.stream = stream
rnd_spk_emb = self.model.sample_random_speaker()
self.params_infer_code = ChatTTS.Chat.InferCodeParams(
spk_emb=rnd_spk_emb,
)
self.warmup()
def warmup(self):
logger.info(f"Warming up {self.__class__.__name__}")
_= self.model.infer("text")
def process(self, llm_sentence):
console.print(f"[green]ASSISTANT: {llm_sentence}")
if self.device == "mps":
import time
start = time.time()
torch.mps.synchronize() # Waits for all kernels in all streams on the MPS device to complete.
torch.mps.empty_cache() # Frees all memory allocated by the MPS device.
_ = (
time.time() - start
) # Removing this line makes it fail more often. I'm looking into it.
wavs_gen = self.model.infer(llm_sentence,params_infer_code=self.params_infer_code, stream=self.stream)
if self.stream:
wavs = [np.array([])]
for gen in wavs_gen:
print('new chunk gen', len(gen[0]))
if len(gen[0]) == 0:
self.should_listen.set()
return
audio_chunk = librosa.resample(gen[0], orig_sr=24000, target_sr=16000)
audio_chunk = (audio_chunk * 32768).astype(np.int16)
print('audio_chunk:', audio_chunk.shape)
while len(audio_chunk) > self.chunk_size:
yield audio_chunk[:self.chunk_size] # 返回前 chunk_size 字节的数据
audio_chunk = audio_chunk[self.chunk_size:] # 移除已返回的数据
yield np.pad(audio_chunk, (0,self.chunk_size-len(audio_chunk)))
else:
print('check result', wavs_gen)
wavs = wavs_gen
if len(wavs[0]) == 0:
self.should_listen.set()
return
audio_chunk = librosa.resample(wavs[0], orig_sr=24000, target_sr=16000)
audio_chunk = (audio_chunk * 32768).astype(np.int16)
print('audio_chunk:', audio_chunk.shape)
for i in range(0, len(audio_chunk), self.chunk_size):
yield np.pad(
audio_chunk[i : i + self.chunk_size],
(0, self.chunk_size - len(audio_chunk[i : i + self.chunk_size])),
)
self.should_listen.set()
