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Merge branch 'bugfix_compute_type' into develop

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This commit is contained in:
Sakamoto Shiina
2025-09-28 02:10:23 +09:00
parent 0bb6bb5977 9a11c6ff9f
commit e7cd872fcc
24 changed files with 861 additions and 192 deletions
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34
src-python/config.py
View File

@@ -11,7 +11,7 @@ from models.translation.translation_languages import translation_lang
from models.translation.translation_utils import ctranslate2_weights
from models.transcription.transcription_languages import transcription_lang
from models.transcription.transcription_whisper import _MODELS as whisper_models
from utils import errorLogging, validateDictStructure
from utils import errorLogging, validateDictStructure, getComputeDeviceList
json_serializable_vars = {}
def json_serializable(var_name):
@@ -818,6 +818,18 @@ class Config:
self._CTRANSLATE2_WEIGHT_TYPE = value
self.saveConfig(inspect.currentframe().f_code.co_name, value)
@property
@json_serializable('SELECTED_TRANSLATION_COMPUTE_TYPE')
def SELECTED_TRANSLATION_COMPUTE_TYPE(self):
return self._SELECTED_TRANSLATION_COMPUTE_TYPE
@SELECTED_TRANSLATION_COMPUTE_TYPE.setter
def SELECTED_TRANSLATION_COMPUTE_TYPE(self, value):
if isinstance(value, str):
if value in self.SELECTED_TRANSLATION_COMPUTE_DEVICE["compute_types"]:
self._SELECTED_TRANSLATION_COMPUTE_TYPE = value
self.saveConfig(inspect.currentframe().f_code.co_name, value)
@property
@json_serializable('WHISPER_WEIGHT_TYPE')
def WHISPER_WEIGHT_TYPE(self):
@@ -830,6 +842,18 @@ class Config:
self._WHISPER_WEIGHT_TYPE = value
self.saveConfig(inspect.currentframe().f_code.co_name, value)
@property
@json_serializable('SELECTED_TRANSCRIPTION_COMPUTE_TYPE')
def SELECTED_TRANSCRIPTION_COMPUTE_TYPE(self):
return self._SELECTED_TRANSCRIPTION_COMPUTE_TYPE
@SELECTED_TRANSCRIPTION_COMPUTE_TYPE.setter
def SELECTED_TRANSCRIPTION_COMPUTE_TYPE(self, value):
if isinstance(value, str):
if value in self.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE["compute_types"]:
self._SELECTED_TRANSCRIPTION_COMPUTE_TYPE = value
self.saveConfig(inspect.currentframe().f_code.co_name, value)
@property
@json_serializable('AUTO_CLEAR_MESSAGE_BOX')
def AUTO_CLEAR_MESSAGE_BOX(self):
@@ -1050,11 +1074,7 @@ class Config:
self._SELECTABLE_TRANSCRIPTION_ENGINE_LIST = list(transcription_lang[list(transcription_lang.keys())[0]].values())[0].keys()
self._SELECTABLE_UI_LANGUAGE_LIST = ["en", "ja", "ko", "zh-Hant", "zh-Hans"]
self._COMPUTE_MODE = "cuda" if torch.cuda.is_available() else "cpu"
self._SELECTABLE_COMPUTE_DEVICE_LIST = []
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
self._SELECTABLE_COMPUTE_DEVICE_LIST.append({"device":"cuda", "device_index": i, "device_name": torch.cuda.get_device_name(i)})
self._SELECTABLE_COMPUTE_DEVICE_LIST.append({"device":"cpu", "device_index": 0, "device_name": "cpu"})
self._SELECTABLE_COMPUTE_DEVICE_LIST = getComputeDeviceList()
self._SEND_MESSAGE_BUTTON_TYPE_LIST = ["show", "hide", "show_and_disable_enter_key"]
self._SEND_MESSAGE_FORMAT_PARTS = {
"message": {
@@ -1186,7 +1206,9 @@ class Config:
self._SELECTED_TRANSLATION_COMPUTE_DEVICE = copy.deepcopy(self.SELECTABLE_COMPUTE_DEVICE_LIST[0])
self._SELECTED_TRANSCRIPTION_COMPUTE_DEVICE = copy.deepcopy(self.SELECTABLE_COMPUTE_DEVICE_LIST[0])
self._CTRANSLATE2_WEIGHT_TYPE = "small"
self._SELECTED_TRANSLATION_COMPUTE_TYPE = "auto"
self._WHISPER_WEIGHT_TYPE = "base"
self._SELECTED_TRANSCRIPTION_COMPUTE_TYPE = "auto"
self._AUTO_CLEAR_MESSAGE_BOX = True
self._SEND_ONLY_TRANSLATED_MESSAGES = False
self._OVERLAY_SMALL_LOG = False

43
src-python/controller.py
View File

@@ -747,13 +747,15 @@ class Controller:
def getSelectedTranslationComputeDevice(*args, **kwargs) -> dict:
return {"status":200, "result":config.SELECTED_TRANSLATION_COMPUTE_DEVICE}
@staticmethod
def setSelectedTranslationComputeDevice(device:str, *args, **kwargs) -> dict:
def setSelectedTranslationComputeDevice(self, device:str, *args, **kwargs) -> dict:
printLog("setSelectedTranslationComputeDevice", device)
pre_device = config.SELECTED_TRANSLATION_COMPUTE_DEVICE
pre_compute_type = config.SELECTED_TRANSLATION_COMPUTE_TYPE
config.SELECTED_TRANSLATION_COMPUTE_DEVICE = device
config.SELECTED_TRANSLATION_COMPUTE_TYPE = "auto"
try:
model.changeTranslatorCTranslate2Model()
self.run(200, self.run_mapping["selected_translation_compute_type"], config.SELECTED_TRANSLATION_COMPUTE_TYPE)
except Exception as e:
# VRAM不足エラーの検出（デバイス切り替え時）
is_vram_error, error_message = model.detectVRAMError(e)
@@ -761,6 +763,7 @@ class Controller:
# 前のデバイス設定に戻す
printLog("VRAM error detected, reverting device setting")
config.SELECTED_TRANSLATION_COMPUTE_DEVICE = pre_device
config.SELECTED_TRANSLATION_COMPUTE_TYPE = pre_compute_type
model.changeTranslatorCTranslate2Model()
else:
# その他のエラーは通常通り処理
@@ -775,10 +778,11 @@ class Controller:
def getSelectedTranscriptionComputeDevice(*args, **kwargs) -> dict:
return {"status":200, "result":config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE}
@staticmethod
def setSelectedTranscriptionComputeDevice(device:str, *args, **kwargs) -> dict:
def setSelectedTranscriptionComputeDevice(self, device:str, *args, **kwargs) -> dict:
printLog("setSelectedTranscriptionComputeDevice", device)
config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE = device
config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE = "auto"
self.run(200, self.run_mapping["selected_transcription_compute_type"], config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE)
return {"status":200,"result":config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE}
@staticmethod
@@ -1549,6 +1553,7 @@ class Controller:
@staticmethod
def setCtranslate2WeightType(data, *args, **kwargs) -> dict:
pre_weight_type = config.CTRANSLATE2_WEIGHT_TYPE
config.CTRANSLATE2_WEIGHT_TYPE = str(data)
if model.checkTranslatorCTranslate2ModelWeight(config.CTRANSLATE2_WEIGHT_TYPE):
def callback():
@@ -1557,8 +1562,29 @@ class Controller:
th_callback.daemon = True
th_callback.start()
th_callback.join()
else:
config.CTRANSLATE2_WEIGHT_TYPE = pre_weight_type
return {"status":200, "result":config.CTRANSLATE2_WEIGHT_TYPE}
@staticmethod
def getSelectedTranslationComputeType(*args, **kwargs) -> dict:
return {"status":200, "result":config.SELECTED_TRANSLATION_COMPUTE_TYPE}
@staticmethod
def setSelectedTranslationComputeType(data, *args, **kwargs) -> dict:
pre_compute_type = config.SELECTED_TRANSLATION_COMPUTE_TYPE
config.SELECTED_TRANSLATION_COMPUTE_TYPE = str(data)
if model.checkTranslatorCTranslate2ModelWeight(config.CTRANSLATE2_WEIGHT_TYPE):
def callback():
model.changeTranslatorCTranslate2Model()
th_callback = Thread(target=callback)
th_callback.daemon = True
th_callback.start()
th_callback.join()
else:
config.SELECTED_TRANSLATION_COMPUTE_TYPE = pre_compute_type
return {"status":200, "result":config.SELECTED_TRANSLATION_COMPUTE_TYPE}
@staticmethod
def getWhisperWeightType(*args, **kwargs) -> dict:
return {"status":200, "result":config.WHISPER_WEIGHT_TYPE}
@@ -1568,6 +1594,15 @@ class Controller:
config.WHISPER_WEIGHT_TYPE = str(data)
return {"status":200, "result": config.WHISPER_WEIGHT_TYPE}
@staticmethod
def getSelectedTranscriptionComputeType(*args, **kwargs) -> dict:
return {"status":200, "result":config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE}
@staticmethod
def setSelectedTranscriptionComputeType(data, *args, **kwargs) -> dict:
config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE = str(data)
return {"status":200, "result":config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE}
@staticmethod
def getSendMessageFormatParts(*args, **kwargs) -> dict:
return {"status":200, "result":config.SEND_MESSAGE_FORMAT_PARTS}

11
src-python/mainloop.py
View File

@@ -48,6 +48,9 @@ run_mapping = {
"selected_translation_engines":"/run/selected_translation_engines",
"translation_engines":"/run/translation_engines",
"selected_translation_compute_type":"/run/selected_translation_compute_type",
"selected_transcription_compute_type":"/run/selected_transcription_compute_type",
"mic_host_list":"/run/mic_host_list",
"mic_device_list":"/run/mic_device_list",
"speaker_device_list":"/run/speaker_device_list",
@@ -162,6 +165,9 @@ mapping = {
"/get/data/ctranslate2_weight_type": {"status": True, "variable":controller.getCtranslate2WeightType},
"/set/data/ctranslate2_weight_type": {"status": True, "variable":controller.setCtranslate2WeightType},
"/get/data/selected_translation_compute_type": {"status": True, "variable":controller.getSelectedTranslationComputeType},
"/set/data/selected_translation_compute_type": {"status": True, "variable":controller.setSelectedTranslationComputeType},
"/run/download_ctranslate2_weight": {"status": True, "variable":controller.downloadCtranslate2Weight},
"/get/data/deepl_auth_key": {"status": False, "variable":controller.getDeepLAuthKey},
@@ -261,8 +267,13 @@ mapping = {
"/set/disable/check_speaker_threshold": {"status": True, "variable":controller.setDisableCheckSpeakerThreshold},
"/get/data/selectable_whisper_weight_type_dict": {"status": True, "variable":controller.getSelectableWhisperWeightTypeDict},
"/get/data/whisper_weight_type": {"status": True, "variable":controller.getWhisperWeightType},
"/set/data/whisper_weight_type": {"status": True, "variable":controller.setWhisperWeightType},
"/get/data/selected_transcription_compute_type": {"status": True, "variable":controller.getSelectedTranscriptionComputeType},
"/set/data/selected_transcription_compute_type": {"status": True, "variable":controller.setSelectedTranscriptionComputeType},
"/run/download_whisper_weight": {"status": True, "variable":controller.downloadWhisperWeight},
# VR

12
src-python/model.py
View File

@@ -112,10 +112,12 @@ class Model:
def changeTranslatorCTranslate2Model(self):
self.translator.changeCTranslate2Model(
config.PATH_LOCAL,
config.CTRANSLATE2_WEIGHT_TYPE,
config.SELECTED_TRANSLATION_COMPUTE_DEVICE["device"],
config.SELECTED_TRANSLATION_COMPUTE_DEVICE["device_index"])
path=config.PATH_LOCAL,
model_type=config.CTRANSLATE2_WEIGHT_TYPE,
device=config.SELECTED_TRANSLATION_COMPUTE_DEVICE["device"],
device_index=config.SELECTED_TRANSLATION_COMPUTE_DEVICE["device_index"],
compute_type=config.SELECTED_TRANSLATION_COMPUTE_TYPE
)
def downloadCTranslate2ModelWeight(self, weight_type, callback=None, end_callback=None):
return downloadCTranslate2Weight(config.PATH_LOCAL, weight_type, callback, end_callback)
@@ -446,6 +448,7 @@ class Model:
whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
device=config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE["device"],
device_index=config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE["device_index"],
compute_type=config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE,
)
def sendMicTranscript():
try:
@@ -629,6 +632,7 @@ class Model:
whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
device=config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE["device"],
device_index=config.SELECTED_TRANSCRIPTION_COMPUTE_DEVICE["device_index"],
compute_type=config.SELECTED_TRANSCRIPTION_COMPUTE_TYPE,
)
def sendSpeakerTranscript():
try:

4
src-python/models/transcription/transcription_transcriber.py
View File

@@ -21,7 +21,7 @@ PHRASE_TIMEOUT = 3
MAX_PHRASES = 10
class AudioTranscriber:
def __init__(self, speaker, source, phrase_timeout, max_phrases, transcription_engine, root=None, whisper_weight_type=None, device="cpu", device_index=0):
def __init__(self, speaker, source, phrase_timeout, max_phrases, transcription_engine, root=None, whisper_weight_type=None, device="cpu", device_index=0, compute_type="auto"):
self.speaker = speaker
self.phrase_timeout = phrase_timeout
self.max_phrases = max_phrases
@@ -41,7 +41,7 @@ class AudioTranscriber:
}
if transcription_engine == "Whisper" and checkWhisperWeight(root, whisper_weight_type) is True:
self.whisper_model = getWhisperModel(root, whisper_weight_type, device=device, device_index=device_index)
self.whisper_model = getWhisperModel(root, whisper_weight_type, device=device, device_index=device_index, compute_type=compute_type)
self.transcription_engine = "Whisper"
def transcribeAudioQueue(self, audio_queue, languages, countries, avg_logprob=-0.8, no_speech_prob=0.6):

5
src-python/models/transcription/transcription_whisper.py
View File

@@ -74,9 +74,10 @@ def downloadWhisperWeight(root, weight_type, callback=None, end_callback=None):
if isinstance(end_callback, Callable):
end_callback()
def getWhisperModel(root, weight_type, device="cpu", device_index=0):
def getWhisperModel(root, weight_type, device="cpu", device_index=0, compute_type="auto"):
path = os_path.join(root, "weights", "whisper", weight_type)
compute_type = getBestComputeType(device, device_index)
if compute_type == "auto":
compute_type = getBestComputeType(device, device_index)
try:
model = WhisperModel(
path,

5
src-python/models/translation/translation_translator.py
View File

@@ -36,14 +36,15 @@ class Translator():
result = False
return result
def changeCTranslate2Model(self, path, model_type, device="cpu", device_index=0):
def changeCTranslate2Model(self, path, model_type, device="cpu", device_index=0, compute_type="auto"):
self.is_loaded_ctranslate2_model = False
directory_name = ctranslate2_weights[model_type]["directory_name"]
tokenizer = ctranslate2_weights[model_type]["tokenizer"]
weight_path = os_path.join(path, "weights", "ctranslate2", directory_name)
tokenizer_path = os_path.join(path, "weights", "ctranslate2", directory_name, "tokenizer")
compute_type = getBestComputeType(device, device_index)
if compute_type == "auto":
compute_type = getBestComputeType(device, device_index)
self.ctranslate2_translator = ctranslate2.Translator(
weight_path,
device=device,

73
src-python/utils.py
View File

@@ -5,6 +5,7 @@ import traceback
import logging
from logging.handlers import RotatingFileHandler
import torch
from ctranslate2 import get_supported_compute_types
import requests
import ipaddress
@@ -78,14 +79,67 @@ def isValidIpAddress(ip_address: str) -> bool:
except ValueError:
return False
def getBestComputeType(device, device_index) -> str:
compute_types = get_supported_compute_types(device, device_index)
compute_types = set(compute_types)
preferred_types = ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"]
def getComputeDeviceList() -> dict:
compute_types = [
{
"device": "cpu",
"device_index": 0,
"device_name": "cpu",
"compute_types": ["auto"] + list(get_supported_compute_types("cpu", 0)),
}
]
for preferred_type in preferred_types:
if preferred_type in compute_types:
return preferred_type
if torch.cuda.is_available():
for device_index in range(torch.cuda.device_count()):
gpu_device_name = torch.cuda.get_device_name(device_index)
gpu_compute_types = ["auto"] + list(get_supported_compute_types("cuda", device_index))
# デバイスごとの計算タイプの制限
if "GTX" in gpu_device_name:
unsupported_types = {"int8_bfloat16", "bfloat16", "float16", "int8"}
gpu_compute_types = [t for t in gpu_compute_types if t not in unsupported_types]
elif not any(keyword in gpu_device_name for keyword in ["RTX", "Tesla", "A100", "Quadro"]):
gpu_compute_types = ["float32"]
compute_types.append(
{
"device": "cuda",
"device_index": device_index,
"device_name": gpu_device_name,
"compute_types": gpu_compute_types,
}
)
return compute_types
def getBestComputeType(device: str, device_index: int) -> str:
compute_types = set(get_supported_compute_types(device, device_index))
device_name = "cpu" if device == "cpu" else torch.cuda.get_device_name(device_index)
# デバイスごとの優先計算タイプ
preferred_types = {
"default": ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"],
"GTX": ["float32"],
"RTX": ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"],
"Tesla": ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"],
"A100": ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"],
"Quadro": ["int8_bfloat16", "int8_float16", "int8", "bfloat16", "float16", "int8_float32", "float32"],
}
# デバイス名に基づいて優先タイプを選択
for key in preferred_types:
if key in device_name:
selected_types = preferred_types[key]
break
else:
selected_types = preferred_types["default"]
# 利用可能な計算タイプを返す
for compute_type in selected_types:
if compute_type in compute_types:
return compute_type
return "float32"
def encodeBase64(data:str) -> dict:
return json.loads(base64.b64decode(data).decode('utf-8'))
@@ -175,4 +229,7 @@ def errorLogging() -> None:
if error_logger is None:
error_logger = setupLogger("error", "error.log", logging.ERROR)
error_logger.error(traceback.format_exc())
error_logger.error(traceback.format_exc())
if __name__ == "__main__":
print(getComputeDeviceList())