venv/lib/python3.13/site-packages/transformers/pipelines/text_to_audio.py

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.from typing import List, Union
from typing import Any, Union, overload

from ..generation import GenerationConfig
from ..utils import is_torch_available
from .base import Pipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING
    from ..models.speecht5.modeling_speecht5 import SpeechT5HifiGan

DEFAULT_VOCODER_ID = "microsoft/speecht5_hifigan"


class TextToAudioPipeline(Pipeline):
    """
    Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`. This
    pipeline generates an audio file from an input text and optional other conditional inputs.

    Unless the model you're using explicitly sets these generation parameters in its configuration files
    (`generation_config.json`), the following default values will be used:
    - max_new_tokens: 256

    Example:

    ```python
    >>> from transformers import pipeline

    >>> pipe = pipeline(model="suno/bark-small")
    >>> output = pipe("Hey it's HuggingFace on the phone!")

    >>> audio = output["audio"]
    >>> sampling_rate = output["sampling_rate"]
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    <Tip>

    You can specify parameters passed to the model by using [`TextToAudioPipeline.__call__.forward_params`] or
    [`TextToAudioPipeline.__call__.generate_kwargs`].

    Example:

    ```python
    >>> from transformers import pipeline

    >>> music_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework="pt")

    >>> # diversify the music generation by adding randomness with a high temperature and set a maximum music length
    >>> generate_kwargs = {
    ...     "do_sample": True,
    ...     "temperature": 0.7,
    ...     "max_new_tokens": 35,
    ... }

    >>> outputs = music_generator("Techno music with high melodic riffs", generate_kwargs=generate_kwargs)
    ```

    </Tip>

    This pipeline can currently be loaded from [`pipeline`] using the following task identifiers: `"text-to-speech"` or
    `"text-to-audio"`.

    See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=text-to-speech).
    """

    # Introducing the processor at load time for new behaviour
    _load_processor = True

    _pipeline_calls_generate = True
    _load_processor = False
    _load_image_processor = False
    _load_feature_extractor = False
    _load_tokenizer = True

    # Make sure the docstring is updated when the default generation config is changed
    _default_generation_config = GenerationConfig(
        max_new_tokens=256,
    )

    def __init__(self, *args, vocoder=None, sampling_rate=None, no_processor=True, **kwargs):
        super().__init__(*args, **kwargs)

        # Legacy behaviour just uses the tokenizer while new models use the processor as a whole at any given time
        self.no_processor = no_processor

        if self.framework == "tf":
            raise ValueError("The TextToAudioPipeline is only available in PyTorch.")

        self.vocoder = None
        if self.model.__class__ in MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING.values():
            self.vocoder = (
                SpeechT5HifiGan.from_pretrained(DEFAULT_VOCODER_ID).to(self.model.device)
                if vocoder is None
                else vocoder
            )

        self.sampling_rate = sampling_rate
        if self.vocoder is not None:
            self.sampling_rate = self.vocoder.config.sampling_rate

        if self.sampling_rate is None:
            # get sampling_rate from config and generation config

            config = self.model.config
            gen_config = self.model.__dict__.get("generation_config", None)
            if gen_config is not None:
                config.update(gen_config.to_dict())

            for sampling_rate_name in ["sample_rate", "sampling_rate"]:
                sampling_rate = getattr(config, sampling_rate_name, None)
                if sampling_rate is not None:
                    self.sampling_rate = sampling_rate
                elif getattr(config, "codec_config", None) is not None:
                    sampling_rate = getattr(config.codec_config, sampling_rate_name, None)
                    if sampling_rate is not None:
                        self.sampling_rate = sampling_rate

        # last fallback to get the sampling rate based on processor
        if self.sampling_rate is None and not self.no_processor and hasattr(self.processor, "feature_extractor"):
            self.sampling_rate = self.processor.feature_extractor.sampling_rate

    def preprocess(self, text, **kwargs):
        if isinstance(text, str):
            text = [text]

        if self.model.config.model_type == "bark":
            # bark Tokenizer is called with BarkProcessor which uses those kwargs
            new_kwargs = {
                "max_length": self.generation_config.semantic_config.get("max_input_semantic_length", 256),
                "add_special_tokens": False,
                "return_attention_mask": True,
                "return_token_type_ids": False,
                "padding": "max_length",
            }

            # priority is given to kwargs
            new_kwargs.update(kwargs)

            kwargs = new_kwargs

        preprocessor = self.tokenizer if self.no_processor else self.processor
        output = preprocessor(text, **kwargs, return_tensors="pt")

        return output

    def _forward(self, model_inputs, **kwargs):
        # we expect some kwargs to be additional tensors which need to be on the right device
        kwargs = self._ensure_tensor_on_device(kwargs, device=self.device)
        forward_params = kwargs["forward_params"]
        generate_kwargs = kwargs["generate_kwargs"]

        if self.model.can_generate():
            # we expect some kwargs to be additional tensors which need to be on the right device
            generate_kwargs = self._ensure_tensor_on_device(generate_kwargs, device=self.device)

            # User-defined `generation_config` passed to the pipeline call take precedence
            if "generation_config" not in generate_kwargs:
                generate_kwargs["generation_config"] = self.generation_config

            # generate_kwargs get priority over forward_params
            forward_params.update(generate_kwargs)

            output = self.model.generate(**model_inputs, **forward_params)
        else:
            if len(generate_kwargs):
                raise ValueError(
                    "You're using the `TextToAudioPipeline` with a forward-only model, but `generate_kwargs` is non "
                    "empty. For forward-only TTA models, please use `forward_params` instead of `generate_kwargs`. "
                    f"For reference, the `generate_kwargs` used here are: {generate_kwargs.keys()}"
                )
            output = self.model(**model_inputs, **forward_params)[0]

        if self.vocoder is not None:
            # in that case, the output is a spectrogram that needs to be converted into a waveform
            output = self.vocoder(output)

        return output

    @overload
    def __call__(self, text_inputs: str, **forward_params: Any) -> dict[str, Any]: ...

    @overload
    def __call__(self, text_inputs: list[str], **forward_params: Any) -> list[dict[str, Any]]: ...

    def __call__(
        self, text_inputs: Union[str, list[str]], **forward_params
    ) -> Union[dict[str, Any], list[dict[str, Any]]]:
        """
        Generates speech/audio from the inputs. See the [`TextToAudioPipeline`] documentation for more information.

        Args:
            text_inputs (`str` or `list[str]`):
                The text(s) to generate.
            forward_params (`dict`, *optional*):
                Parameters passed to the model generation/forward method. `forward_params` are always passed to the
                underlying model.
            generate_kwargs (`dict`, *optional*):
                The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a
                complete overview of generate, check the [following
                guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation). `generate_kwargs` are
                only passed to the underlying model if the latter is a generative model.

        Return:
            A `dict` or a list of `dict`: The dictionaries have two keys:

            - **audio** (`np.ndarray` of shape `(nb_channels, audio_length)`) -- The generated audio waveform.
            - **sampling_rate** (`int`) -- The sampling rate of the generated audio waveform.
        """
        return super().__call__(text_inputs, **forward_params)

    def _sanitize_parameters(
        self,
        preprocess_params=None,
        forward_params=None,
        generate_kwargs=None,
    ):
        if getattr(self, "assistant_model", None) is not None:
            generate_kwargs["assistant_model"] = self.assistant_model
        if getattr(self, "assistant_tokenizer", None) is not None:
            generate_kwargs["tokenizer"] = self.tokenizer
            generate_kwargs["assistant_tokenizer"] = self.assistant_tokenizer

        params = {
            "forward_params": forward_params if forward_params else {},
            "generate_kwargs": generate_kwargs if generate_kwargs else {},
        }

        if preprocess_params is None:
            preprocess_params = {}
        postprocess_params = {}

        return preprocess_params, params, postprocess_params

    def postprocess(self, audio):
        output_dict = {}

        if self.model.config.model_type == "csm":
            waveform_key = "audio"
        else:
            waveform_key = "waveform"

        # We directly get the waveform
        if self.no_processor:
            if isinstance(audio, dict):
                waveform = audio[waveform_key]
            elif isinstance(audio, tuple):
                waveform = audio[0]
            else:
                waveform = audio
        # Or we need to postprocess to get the waveform
        else:
            waveform = self.processor.decode(audio)

        if isinstance(audio, list):
            output_dict["audio"] = [el.to(device="cpu", dtype=torch.float).numpy() for el in waveform]
        else:
            output_dict["audio"] = waveform.to(device="cpu", dtype=torch.float).numpy()
        output_dict["sampling_rate"] = self.sampling_rate

        return output_dict