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chore: optimize streaming tts of xinference (#6966)

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takatost 2024-08-05 18:23:23 +08:00 committed by GitHub
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2 changed files with 78 additions and 64 deletions
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122
api/core/model_runtime/model_providers/xinference/tts/tts.py
View File

@ -1,11 +1,7 @@
import concurrent.futures
from functools import reduce
from io import BytesIO
from typing import Optional
from flask import Response
from pydub import AudioSegment
from xinference_client.client.restful.restful_client import Client, RESTfulAudioModelHandle
from xinference_client.client.restful.restful_client import RESTfulAudioModelHandle
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
@ -19,6 +15,7 @@ from core.model_runtime.errors.invoke import (
)
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.tts_model import TTSModel
from core.model_runtime.model_providers.xinference.xinference_helper import XinferenceHelper
class XinferenceText2SpeechModel(TTSModel):
@ -26,7 +23,12 @@ class XinferenceText2SpeechModel(TTSModel):
def __init__(self):
# preset voices, need support custom voice
self.model_voices = {
'chattts': {
'__default': {
'all': [
{'name': 'Default', 'value': 'default'},
]
},
'ChatTTS': {
'all': [
{'name': 'Alloy', 'value': 'alloy'},
{'name': 'Echo', 'value': 'echo'},
@ -36,7 +38,7 @@ class XinferenceText2SpeechModel(TTSModel):
{'name': 'Shimmer', 'value': 'shimmer'},
]
},
'cosyvoice': {
'CosyVoice': {
'zh-Hans': [
{'name': '中文男', 'value': '中文男'},
{'name': '中文女', 'value': '中文女'},
@ -77,18 +79,21 @@ class XinferenceText2SpeechModel(TTSModel):
if credentials['server_url'].endswith('/'):
credentials['server_url'] = credentials['server_url'][:-1]
# initialize client
client = Client(
base_url=credentials['server_url']
extra_param = XinferenceHelper.get_xinference_extra_parameter(
server_url=credentials['server_url'],
model_uid=credentials['model_uid']
)
xinference_client = client.get_model(model_uid=credentials['model_uid'])
if not isinstance(xinference_client, RESTfulAudioModelHandle):
if 'text-to-audio' not in extra_param.model_ability:
raise InvokeBadRequestError(
'please check model type, the model you want to invoke is not a audio model')
'please check model type, the model you want to invoke is not a text-to-audio model')
self._tts_invoke(
if extra_param.model_family and extra_param.model_family in self.model_voices:
credentials['audio_model_name'] = extra_param.model_family
else:
credentials['audio_model_name'] = '__default'
self._tts_invoke_streaming(
model=model,
credentials=credentials,
content_text='Hello Dify!',
@ -110,7 +115,7 @@ class XinferenceText2SpeechModel(TTSModel):
:param user: unique user id
:return: text translated to audio file
"""
return self._tts_invoke(model, credentials, content_text, voice)
return self._tts_invoke_streaming(model, credentials, content_text, voice)
def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
"""
@ -161,13 +166,15 @@ class XinferenceText2SpeechModel(TTSModel):
}
def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
audio_model_name = credentials.get('audio_model_name', '__default')
for key, voices in self.model_voices.items():
if key in model.lower():
if language in voices:
if key in audio_model_name:
if language and language in voices:
return voices[language]
elif 'all' in voices:
return voices['all']
return []
return self.model_voices['__default']['all']
def _get_model_default_voice(self, model: str, credentials: dict) -> any:
return ""
@ -181,60 +188,55 @@ class XinferenceText2SpeechModel(TTSModel):
def _get_model_workers_limit(self, model: str, credentials: dict) -> int:
return 5
def _tts_invoke(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str,
voice: str) -> any:
"""
_tts_invoke text2speech model
_tts_invoke_streaming text2speech model
:param model: model name
:param credentials: model credentials
:param voice: model timbre
:param content_text: text content to be translated
:param voice: model timbre
:return: text translated to audio file
"""
if credentials['server_url'].endswith('/'):
credentials['server_url'] = credentials['server_url'][:-1]
word_limit = self._get_model_word_limit(model, credentials)
audio_type = self._get_model_audio_type(model, credentials)
handle = RESTfulAudioModelHandle(credentials['model_uid'], credentials['server_url'], auth_headers={})
try:
sentences = list(self._split_text_into_sentences(org_text=content_text, max_length=word_limit))
audio_bytes_list = []
handle = RESTfulAudioModelHandle(credentials['model_uid'], credentials['server_url'], auth_headers={})
with concurrent.futures.ThreadPoolExecutor(max_workers=min((3, len(sentences)))) as executor:
model_support_voice = [x.get("value") for x in
self.get_tts_model_voices(model=model, credentials=credentials)]
if not voice or voice not in model_support_voice:
voice = self._get_model_default_voice(model, credentials)
word_limit = self._get_model_word_limit(model, credentials)
if len(content_text) > word_limit:
sentences = self._split_text_into_sentences(content_text, max_length=word_limit)
executor = concurrent.futures.ThreadPoolExecutor(max_workers=min(3, len(sentences)))
futures = [executor.submit(
handle.speech, input=sentence, voice=voice, response_format="mp3", speed=1.0, stream=False)
for sentence in sentences]
for future in futures:
try:
if future.result():
audio_bytes_list.append(future.result())
except Exception as ex:
raise InvokeBadRequestError(str(ex))
handle.speech,
input=sentences[i],
voice=voice,
response_format="mp3",
speed=1.0,
stream=False
)
for i in range(len(sentences))]
if len(audio_bytes_list) > 0:
audio_segments = [AudioSegment.from_file(
BytesIO(audio_bytes), format=audio_type) for audio_bytes in
audio_bytes_list if audio_bytes]
combined_segment = reduce(lambda x, y: x + y, audio_segments)
buffer: BytesIO = BytesIO()
combined_segment.export(buffer, format=audio_type)
buffer.seek(0)
return Response(buffer.read(), status=200, mimetype=f"audio/{audio_type}")
for index, future in enumerate(futures):
response = future.result()
for i in range(0, len(response), 1024):
yield response[i:i + 1024]
else:
response = handle.speech(
input=content_text.strip(),
voice=voice,
response_format="mp3",
speed=1.0,
stream=False
)
for i in range(0, len(response), 1024):
yield response[i:i + 1024]
except Exception as ex:
raise InvokeBadRequestError(str(ex))
def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
"""
_tts_invoke_streaming text2speech model
Attention: stream api may return error [Parallel generation is not supported by ggml]
:param model: model name
:param credentials: model credentials
:param voice: model timbre
:param content_text: text content to be translated
:return: text translated to audio file
"""
pass

20
api/core/model_runtime/model_providers/xinference/xinference_helper.py
View File

@ -1,5 +1,6 @@
from threading import Lock
from time import time
from typing import Optional
from requests.adapters import HTTPAdapter
from requests.exceptions import ConnectionError, MissingSchema, Timeout
@ -15,9 +16,11 @@ class XinferenceModelExtraParameter:
context_length: int = 2048
support_function_call: bool = False
support_vision: bool = False
model_family: Optional[str]
def __init__(self, model_format: str, model_handle_type: str, model_ability: list[str],
support_function_call: bool, support_vision: bool, max_tokens: int, context_length: int) -> None:
support_function_call: bool, support_vision: bool, max_tokens: int, context_length: int,
model_family: Optional[str]) -> None:
self.model_format = model_format
self.model_handle_type = model_handle_type
self.model_ability = model_ability
@ -25,6 +28,7 @@ class XinferenceModelExtraParameter:
self.support_vision = support_vision
self.max_tokens = max_tokens
self.context_length = context_length
self.model_family = model_family
cache = {}
cache_lock = Lock()
@ -78,9 +82,16 @@ class XinferenceHelper:
model_format = response_json.get('model_format', 'ggmlv3')
model_ability = response_json.get('model_ability', [])
model_family = response_json.get('model_family', None)
if response_json.get('model_type') == 'embedding':
model_handle_type = 'embedding'
elif response_json.get('model_type') == 'audio':
model_handle_type = 'audio'
if model_family and model_family in ['ChatTTS', 'CosyVoice']:
model_ability.append('text-to-audio')
else:
model_ability.append('audio-to-text')
elif model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
model_handle_type = 'chatglm'
elif 'generate' in model_ability:
@ -88,7 +99,7 @@ class XinferenceHelper:
elif 'chat' in model_ability:
model_handle_type = 'chat'
else:
raise NotImplementedError(f'xinference model handle type {model_handle_type} is not supported')
raise NotImplementedError('xinference model handle type is not supported')
support_function_call = 'tools' in model_ability
support_vision = 'vision' in model_ability
@ -103,5 +114,6 @@ class XinferenceHelper:
support_function_call=support_function_call,
support_vision=support_vision,
max_tokens=max_tokens,
context_length=context_length
)
context_length=context_length,
model_family=model_family
)