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modify audio handle

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brige 2024-10-16 19:04:12 +08:00
parent da37374232
commit d8225d3929
5 changed files with 63 additions and 20 deletions
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12
human/audio_handler.py
View File

@ -1,6 +1,9 @@
#encoding = utf8
import logging
from abc import ABC, abstractmethod
logger = logging.getLogger(__name__)
class AudioHandler(ABC):
def __init__(self, context, handler):
@ -8,6 +11,11 @@ class AudioHandler(ABC):
self._handler = handler
@abstractmethod
def on_handle(self, stream, index):
def on_handle(self, stream, index):
pass
def on_next_handle(self, stream, type_):
if self._handler is not None:
self._handler.on_handle(stream, index)
self._handler.on_handle(stream, type_)
else:
logging.info(f'_handler is None')

28
human/audio_inference_handler.py
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@ -1,6 +1,7 @@
#encoding = utf8
import queue
import time
from queue import Queue
from threading import Event, Thread
import numpy as np
@ -14,20 +15,25 @@ class AudioInferenceHandler(AudioHandler):
def __init__(self, context, handler):
super().__init__(context, handler)
self._mal_queue = Queue()
self._audio_queue = Queue()
self._exit_event = Event()
self._run_thread = Thread(target=self.__on_run)
self._exit_event.set()
self._run_thread.start()
def on_handle(self, stream, index):
if self._handler is not None:
self._handler.on_handle(stream, index)
def on_handle(self, stream, type_):
if type_ == 1:
self._mal_queue.put(stream)
elif type_ == 0:
self._audio_queue.put(stream)
def __on_run(self):
model = load_model(r'.\checkpoints\wav2lip.pth')
print("Model loaded")
face_list_cycle = self._human.get_face_list_cycle()
face_list_cycle = self._context.face_list_cycle()
length = len(face_list_cycle)
index = 0
@ -43,20 +49,21 @@ class AudioInferenceHandler(AudioHandler):
start_time = time.perf_counter()
batch_size = self._context.batch_size()
try:
mel_batch = self._feat_queue.get(block=True, timeout=0.1)
mel_batch = self._mal_queue.get(block=True, timeout=0.1)
except queue.Empty:
continue
is_all_silence = True
audio_frames = []
for _ in range(batch_size * 2):
frame, type_ = self._audio_out_queue.get()
frame, type_ = self._audio_queue.get()
audio_frames.append((frame, type_))
if type_ == 0:
is_all_silence = False
if is_all_silence:
for i in range(batch_size):
self._human.push_res_frame(None, mirror_index(length, index), audio_frames[i * 2:i * 2 + 2])
self.on_next_handle((None, mirror_index(length, index), audio_frames[i * 2:i * 2 + 2]),
0)
index = index + 1
else:
print('infer=======')
@ -71,7 +78,7 @@ class AudioInferenceHandler(AudioHandler):
mel_batch = np.asarray(mel_batch)
img_masked = img_batch.copy()
img_masked[:, face.shape[0] // 2:] = 0
#
img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255.
mel_batch = np.reshape(mel_batch,
[len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
@ -94,8 +101,9 @@ class AudioInferenceHandler(AudioHandler):
image_index = 0
for i, res_frame in enumerate(pred):
self._human.push_res_frame(res_frame, mirror_index(length, index),
audio_frames[i * 2:i * 2 + 2])
self.on_next_handle(
(res_frame, mirror_index(length, index), audio_frames[i * 2:i * 2 + 2]),
0)
index = index + 1
image_index = image_index + 1
print('batch count', image_index)

10
human/audio_mal_handler.py
View File

@ -27,8 +27,7 @@ class AudioMalHandler(AudioHandler):
self.chunk = context.sample_rate() // context.fps()
def on_handle(self, stream, index):
if self._handler is not None:
self._handler.on_handle(stream, index)
self._queue.put(stream)
def _on_run(self):
logging.info('chunk2mal run')
@ -42,9 +41,7 @@ class AudioMalHandler(AudioHandler):
for _ in range(self._context.batch_size() * 2):
frame, _type = self.get_audio_frame()
self.frames.append(frame)
# put to output
# self.output_queue.put((frame, _type))
self._human.push_out_put(frame, _type)
self.on_next_handle((frame, _type), 0)
# context not enough, do not run network.
if len(self.frames) <= self._context.stride_left_size() + self._context.stride_right_size():
return
@ -67,8 +64,7 @@ class AudioMalHandler(AudioHandler):
else:
mel_chunks.append(mel[:, start_idx: start_idx + mel_step_size])
i += 1
# self.feat_queue.put(mel_chunks)
self._human.push_mel_chunks(mel_chunks)
self.on_next_handle(mel_chunks, 1)
# discard the old part to save memory
self.frames = self.frames[-(self._context.stride_left_size() + self._context.stride_right_size()):]

17
human/human_context.py
View File

@ -4,6 +4,7 @@ import logging
from asr import SherpaNcnnAsr
from nlp import PunctuationSplit, DouBao
from tts import TTSEdge, TTSAudioSplitHandle
from utils import load_avatar, get_device
logger = logging.getLogger(__name__)
@ -11,12 +12,14 @@ logger = logging.getLogger(__name__)
class HumanContext:
def __init__(self):
self._fps = 50 # 20 ms per frame
self._image_size = 96
self._batch_size = 16
self._sample_rate = 16000
self._stride_left_size = 10
self._stride_right_size = 10
full_images, face_frames, coord_frames = load_avatar(r'./face/')
self._device = get_device()
full_images, face_frames, coord_frames = load_avatar(r'./face/', self._device, self._image_size)
self._frame_list_cycle = full_images
self._face_list_cycle = face_frames
self._coord_list_cycle = coord_frames
@ -28,6 +31,14 @@ class HumanContext:
def fps(self):
return self._fps
@property
def image_size(self):
return self._image_size
@property
def device(self):
return self._device
@property
def batch_size(self):
return self._batch_size
@ -44,6 +55,10 @@ class HumanContext:
def stride_right_size(self):
return self._stride_right_size
@property
def face_list_cycle(self):
return self._face_list_cycle
def build(self):
tts_handle = TTSAudioSplitHandle(self, None)
tts = TTSEdge(tts_handle)

16
human/human_render.py Normal file
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@ -0,0 +1,16 @@
#encoding = utf8
from queue import Queue
from human import AudioHandler
class HumanRender(AudioHandler):
def __init__(self, context, handler):
super().__init__(context, handler)
self._queue = Queue(context.batch_size * 2)
def on_handle(self, stream, index):
self._queue.put(stream)