# This file was auto-generated by Fern from our API Definition. import typing from ..core.client_wrapper import AsyncClientWrapper, SyncClientWrapper from ..core.request_options import RequestOptions from ..types.audio_with_timestamps_and_voice_segments_response_model import ( AudioWithTimestampsAndVoiceSegmentsResponseModel, ) from ..types.dialogue_input import DialogueInput from ..types.model_settings_response_model import ModelSettingsResponseModel from ..types.pronunciation_dictionary_version_locator import PronunciationDictionaryVersionLocator from ..types.streaming_audio_chunk_with_timestamps_and_voice_segments_response_model import ( StreamingAudioChunkWithTimestampsAndVoiceSegmentsResponseModel, ) from .raw_client import AsyncRawTextToDialogueClient, RawTextToDialogueClient from .types.body_text_to_dialogue_full_with_timestamps_apply_text_normalization import ( BodyTextToDialogueFullWithTimestampsApplyTextNormalization, ) from .types.body_text_to_dialogue_multi_voice_streaming_v_1_text_to_dialogue_stream_post_apply_text_normalization import ( BodyTextToDialogueMultiVoiceStreamingV1TextToDialogueStreamPostApplyTextNormalization, ) from .types.body_text_to_dialogue_multi_voice_v_1_text_to_dialogue_post_apply_text_normalization import ( BodyTextToDialogueMultiVoiceV1TextToDialoguePostApplyTextNormalization, ) from .types.body_text_to_dialogue_stream_with_timestamps_apply_text_normalization import ( BodyTextToDialogueStreamWithTimestampsApplyTextNormalization, ) from .types.text_to_dialogue_convert_request_output_format import TextToDialogueConvertRequestOutputFormat from .types.text_to_dialogue_convert_with_timestamps_request_output_format import ( TextToDialogueConvertWithTimestampsRequestOutputFormat, ) from .types.text_to_dialogue_stream_request_output_format import TextToDialogueStreamRequestOutputFormat from .types.text_to_dialogue_stream_with_timestamps_request_output_format import ( TextToDialogueStreamWithTimestampsRequestOutputFormat, ) # this is used as the default value for optional parameters OMIT = typing.cast(typing.Any, ...) class TextToDialogueClient: def __init__(self, *, client_wrapper: SyncClientWrapper): self._raw_client = RawTextToDialogueClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> RawTextToDialogueClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- RawTextToDialogueClient """ return self._raw_client def convert( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueConvertRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[ BodyTextToDialogueMultiVoiceV1TextToDialoguePostApplyTextNormalization ] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Iterator[bytes]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns audio. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueConvertRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueMultiVoiceV1TextToDialoguePostApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Returns ------- typing.Iterator[bytes] The generated audio file Examples -------- from elevenlabs import DialogueInput, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.text_to_dialogue.convert( inputs=[ DialogueInput( text="Knock knock", voice_id="JBFqnCBsd6RMkjVDRZzb", ), DialogueInput( text="Who is there?", voice_id="Aw4FAjKCGjjNkVhN1Xmq", ), ], ) """ with self._raw_client.convert( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: yield from r.data def stream( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueStreamRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[ BodyTextToDialogueMultiVoiceStreamingV1TextToDialogueStreamPostApplyTextNormalization ] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Iterator[bytes]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns an audio stream. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueStreamRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueMultiVoiceStreamingV1TextToDialogueStreamPostApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Returns ------- typing.Iterator[bytes] Streaming audio data Examples -------- from elevenlabs import DialogueInput, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.text_to_dialogue.stream( inputs=[ DialogueInput( text="Knock knock", voice_id="JBFqnCBsd6RMkjVDRZzb", ), DialogueInput( text="Who is there?", voice_id="Aw4FAjKCGjjNkVhN1Xmq", ), ], ) """ with self._raw_client.stream( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: yield from r.data def stream_with_timestamps( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueStreamWithTimestampsRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[BodyTextToDialogueStreamWithTimestampsApplyTextNormalization] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.Iterator[StreamingAudioChunkWithTimestampsAndVoiceSegmentsResponseModel]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns a stream of JSON blobs containing audio as a base64 encoded string and timestamps Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueStreamWithTimestampsRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueStreamWithTimestampsApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. Yields ------ typing.Iterator[StreamingAudioChunkWithTimestampsAndVoiceSegmentsResponseModel] Stream of transcription chunks Examples -------- from elevenlabs import DialogueInput, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) response = client.text_to_dialogue.stream_with_timestamps( output_format="mp3_22050_32", inputs=[ DialogueInput( text="Hello, how are you?", voice_id="bYTqZQo3Jz7LQtmGTgwi", ), DialogueInput( text="I'm doing well, thank you!", voice_id="6lCwbsX1yVjD49QmpkTR", ), ], ) for chunk in response: yield chunk """ with self._raw_client.stream_with_timestamps( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: yield from r.data def convert_with_timestamps( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueConvertWithTimestampsRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[BodyTextToDialogueFullWithTimestampsApplyTextNormalization] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AudioWithTimestampsAndVoiceSegmentsResponseModel: """ Generate dialogue from text with precise character-level timing information for audio-text synchronization. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueConvertWithTimestampsRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueFullWithTimestampsApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AudioWithTimestampsAndVoiceSegmentsResponseModel Successful Response Examples -------- from elevenlabs import DialogueInput, ElevenLabs client = ElevenLabs( api_key="YOUR_API_KEY", ) client.text_to_dialogue.convert_with_timestamps( output_format="mp3_22050_32", inputs=[ DialogueInput( text="Hello, how are you?", voice_id="bYTqZQo3Jz7LQtmGTgwi", ), DialogueInput( text="I'm doing well, thank you!", voice_id="6lCwbsX1yVjD49QmpkTR", ), ], ) """ _response = self._raw_client.convert_with_timestamps( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) return _response.data class AsyncTextToDialogueClient: def __init__(self, *, client_wrapper: AsyncClientWrapper): self._raw_client = AsyncRawTextToDialogueClient(client_wrapper=client_wrapper) @property def with_raw_response(self) -> AsyncRawTextToDialogueClient: """ Retrieves a raw implementation of this client that returns raw responses. Returns ------- AsyncRawTextToDialogueClient """ return self._raw_client async def convert( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueConvertRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[ BodyTextToDialogueMultiVoiceV1TextToDialoguePostApplyTextNormalization ] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.AsyncIterator[bytes]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns audio. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueConvertRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueMultiVoiceV1TextToDialoguePostApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Returns ------- typing.AsyncIterator[bytes] The generated audio file Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, DialogueInput client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.text_to_dialogue.convert( inputs=[ DialogueInput( text="Knock knock", voice_id="JBFqnCBsd6RMkjVDRZzb", ), DialogueInput( text="Who is there?", voice_id="Aw4FAjKCGjjNkVhN1Xmq", ), ], ) asyncio.run(main()) """ async with self._raw_client.convert( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: async for _chunk in r.data: yield _chunk async def stream( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueStreamRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[ BodyTextToDialogueMultiVoiceStreamingV1TextToDialogueStreamPostApplyTextNormalization ] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.AsyncIterator[bytes]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns an audio stream. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueStreamRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueMultiVoiceStreamingV1TextToDialogueStreamPostApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. You can pass in configuration such as `chunk_size`, and more to customize the request and response. Returns ------- typing.AsyncIterator[bytes] Streaming audio data Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, DialogueInput client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.text_to_dialogue.stream( inputs=[ DialogueInput( text="Knock knock", voice_id="JBFqnCBsd6RMkjVDRZzb", ), DialogueInput( text="Who is there?", voice_id="Aw4FAjKCGjjNkVhN1Xmq", ), ], ) asyncio.run(main()) """ async with self._raw_client.stream( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: async for _chunk in r.data: yield _chunk async def stream_with_timestamps( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueStreamWithTimestampsRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[BodyTextToDialogueStreamWithTimestampsApplyTextNormalization] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> typing.AsyncIterator[StreamingAudioChunkWithTimestampsAndVoiceSegmentsResponseModel]: """ Converts a list of text and voice ID pairs into speech (dialogue) and returns a stream of JSON blobs containing audio as a base64 encoded string and timestamps Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueStreamWithTimestampsRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueStreamWithTimestampsApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. Yields ------ typing.AsyncIterator[StreamingAudioChunkWithTimestampsAndVoiceSegmentsResponseModel] Stream of transcription chunks Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, DialogueInput client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: response = await client.text_to_dialogue.stream_with_timestamps( output_format="mp3_22050_32", inputs=[ DialogueInput( text="Hello, how are you?", voice_id="bYTqZQo3Jz7LQtmGTgwi", ), DialogueInput( text="I'm doing well, thank you!", voice_id="6lCwbsX1yVjD49QmpkTR", ), ], ) async for chunk in response: yield chunk asyncio.run(main()) """ async with self._raw_client.stream_with_timestamps( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) as r: async for _chunk in r.data: yield _chunk async def convert_with_timestamps( self, *, inputs: typing.Sequence[DialogueInput], output_format: typing.Optional[TextToDialogueConvertWithTimestampsRequestOutputFormat] = None, model_id: typing.Optional[str] = OMIT, language_code: typing.Optional[str] = OMIT, settings: typing.Optional[ModelSettingsResponseModel] = OMIT, pronunciation_dictionary_locators: typing.Optional[ typing.Sequence[PronunciationDictionaryVersionLocator] ] = OMIT, seed: typing.Optional[int] = OMIT, apply_text_normalization: typing.Optional[BodyTextToDialogueFullWithTimestampsApplyTextNormalization] = OMIT, request_options: typing.Optional[RequestOptions] = None, ) -> AudioWithTimestampsAndVoiceSegmentsResponseModel: """ Generate dialogue from text with precise character-level timing information for audio-text synchronization. Parameters ---------- inputs : typing.Sequence[DialogueInput] A list of dialogue inputs, each containing text and a voice ID which will be converted into speech. output_format : typing.Optional[TextToDialogueConvertWithTimestampsRequestOutputFormat] Output format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pro tier or above. Note that the μ-law format (sometimes written mu-law, often approximated as u-law) is commonly used for Twilio audio inputs. model_id : typing.Optional[str] Identifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property. language_code : typing.Optional[str] Language code (ISO 639-1) used to enforce a language for the model and text normalization. If the model does not support provided language code, an error will be returned. settings : typing.Optional[ModelSettingsResponseModel] Settings controlling the dialogue generation. pronunciation_dictionary_locators : typing.Optional[typing.Sequence[PronunciationDictionaryVersionLocator]] A list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request seed : typing.Optional[int] If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed. Must be integer between 0 and 4294967295. apply_text_normalization : typing.Optional[BodyTextToDialogueFullWithTimestampsApplyTextNormalization] This parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- AudioWithTimestampsAndVoiceSegmentsResponseModel Successful Response Examples -------- import asyncio from elevenlabs import AsyncElevenLabs, DialogueInput client = AsyncElevenLabs( api_key="YOUR_API_KEY", ) async def main() -> None: await client.text_to_dialogue.convert_with_timestamps( output_format="mp3_22050_32", inputs=[ DialogueInput( text="Hello, how are you?", voice_id="bYTqZQo3Jz7LQtmGTgwi", ), DialogueInput( text="I'm doing well, thank you!", voice_id="6lCwbsX1yVjD49QmpkTR", ), ], ) asyncio.run(main()) """ _response = await self._raw_client.convert_with_timestamps( inputs=inputs, output_format=output_format, model_id=model_id, language_code=language_code, settings=settings, pronunciation_dictionary_locators=pronunciation_dictionary_locators, seed=seed, apply_text_normalization=apply_text_normalization, request_options=request_options, ) return _response.data