feat:support azure tts (#2751)
呆萌闷油瓶
2 years ago
committed by
GitHub
parent
796c5626a7
commit
f49b1afd6c
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import concurrent.futures
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import copy
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from functools import reduce
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from io import BytesIO
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from typing import Optional
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from flask import Response, stream_with_context
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from openai import AzureOpenAI
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from pydub import AudioSegment
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from core.model_runtime.entities.model_entities import AIModelEntity
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from core.model_runtime.errors.invoke import InvokeBadRequestError
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.__base.tts_model import TTSModel
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from core.model_runtime.model_providers.azure_openai._common import _CommonAzureOpenAI
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from core.model_runtime.model_providers.azure_openai._constant import TTS_BASE_MODELS, AzureBaseModel
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from extensions.ext_storage import storage
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class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
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"""
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Model class for OpenAI Speech to text model.
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"""
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def _invoke(self, model: str, tenant_id: str, credentials: dict,
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content_text: str, voice: str, streaming: bool, user: Optional[str] = None) -> any:
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"""
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_invoke text2speech model
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:param model: model name
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:param tenant_id: user tenant id
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:param credentials: model credentials
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:param content_text: text content to be translated
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:param voice: model timbre
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:param streaming: output is streaming
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:param user: unique user id
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:return: text translated to audio file
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"""
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audio_type = self._get_model_audio_type(model, credentials)
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if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
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voice = self._get_model_default_voice(model, credentials)
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if streaming:
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return Response(stream_with_context(self._tts_invoke_streaming(model=model,
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credentials=credentials,
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content_text=content_text,
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tenant_id=tenant_id,
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voice=voice)),
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status=200, mimetype=f'audio/{audio_type}')
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else:
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return self._tts_invoke(model=model, credentials=credentials, content_text=content_text, voice=voice)
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def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
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"""
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validate credentials text2speech model
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:param model: model name
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:param credentials: model credentials
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:param user: unique user id
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:return: text translated to audio file
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"""
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try:
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self._tts_invoke(
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model=model,
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credentials=credentials,
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content_text='Hello Dify!',
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voice=self._get_model_default_voice(model, credentials),
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)
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except Exception as ex:
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raise CredentialsValidateFailedError(str(ex))
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def _tts_invoke(self, model: str, credentials: dict, content_text: str, voice: str) -> Response:
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"""
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_tts_invoke text2speech model
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:param model: model name
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:param credentials: model credentials
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:param content_text: text content to be translated
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:param voice: model timbre
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:return: text translated to audio file
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"""
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audio_type = self._get_model_audio_type(model, credentials)
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word_limit = self._get_model_word_limit(model, credentials)
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max_workers = self._get_model_workers_limit(model, credentials)
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try:
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sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
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audio_bytes_list = list()
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# Create a thread pool and map the function to the list of sentences
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with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
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futures = [executor.submit(self._process_sentence, sentence=sentence, model=model, voice=voice,
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credentials=credentials) for sentence in sentences]
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for future in futures:
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try:
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if future.result():
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audio_bytes_list.append(future.result())
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except Exception as ex:
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raise InvokeBadRequestError(str(ex))
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if len(audio_bytes_list) > 0:
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audio_segments = [AudioSegment.from_file(BytesIO(audio_bytes), format=audio_type) for audio_bytes in
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audio_bytes_list if audio_bytes]
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combined_segment = reduce(lambda x, y: x + y, audio_segments)
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buffer: BytesIO = BytesIO()
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combined_segment.export(buffer, format=audio_type)
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buffer.seek(0)
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return Response(buffer.read(), status=200, mimetype=f"audio/{audio_type}")
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except Exception as ex:
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raise InvokeBadRequestError(str(ex))
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# Todo: To improve the streaming function
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def _tts_invoke_streaming(self, model: str, tenant_id: str, credentials: dict, content_text: str,
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voice: str) -> any:
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"""
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_tts_invoke_streaming text2speech model
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:param model: model name
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:param tenant_id: user tenant id
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:param credentials: model credentials
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:param content_text: text content to be translated
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:param voice: model timbre
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:return: text translated to audio file
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"""
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# transform credentials to kwargs for model instance
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credentials_kwargs = self._to_credential_kwargs(credentials)
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if not voice or voice not in self.get_tts_model_voices(model=model, credentials=credentials):
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voice = self._get_model_default_voice(model, credentials)
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word_limit = self._get_model_word_limit(model, credentials)
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audio_type = self._get_model_audio_type(model, credentials)
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tts_file_id = self._get_file_name(content_text)
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file_path = f'generate_files/audio/{tenant_id}/{tts_file_id}.{audio_type}'
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try:
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client = AzureOpenAI(**credentials_kwargs)
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sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
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for sentence in sentences:
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response = client.audio.speech.create(model=model, voice=voice, input=sentence.strip())
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# response.stream_to_file(file_path)
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storage.save(file_path, response.read())
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except Exception as ex:
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raise InvokeBadRequestError(str(ex))
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def _process_sentence(self, sentence: str, model: str,
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voice, credentials: dict):
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"""
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_tts_invoke openai text2speech model api
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:param model: model name
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:param credentials: model credentials
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:param voice: model timbre
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:param sentence: text content to be translated
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:return: text translated to audio file
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"""
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# transform credentials to kwargs for model instance
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credentials_kwargs = self._to_credential_kwargs(credentials)
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client = AzureOpenAI(**credentials_kwargs)
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response = client.audio.speech.create(model=model, voice=voice, input=sentence.strip())
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if isinstance(response.read(), bytes):
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return response.read()
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def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
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ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
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return ai_model_entity.entity
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@staticmethod
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def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel:
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for ai_model_entity in TTS_BASE_MODELS:
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if ai_model_entity.base_model_name == base_model_name:
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ai_model_entity_copy = copy.deepcopy(ai_model_entity)
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ai_model_entity_copy.entity.model = model
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ai_model_entity_copy.entity.label.en_US = model
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ai_model_entity_copy.entity.label.zh_Hans = model
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return ai_model_entity_copy
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return None
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