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| #!/usr/bin/env python | |
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. 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. | |
| import torch | |
| from ..models.speecht5 import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor | |
| from ..utils import is_datasets_available | |
| from .base import PipelineTool | |
| if is_datasets_available(): | |
| from datasets import load_dataset | |
| class TextToSpeechTool(PipelineTool): | |
| default_checkpoint = "microsoft/speecht5_tts" | |
| description = ( | |
| "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the " | |
| "text to read (in English) and returns a waveform object containing the sound." | |
| ) | |
| name = "text_reader" | |
| pre_processor_class = SpeechT5Processor | |
| model_class = SpeechT5ForTextToSpeech | |
| post_processor_class = SpeechT5HifiGan | |
| inputs = ["text"] | |
| outputs = ["audio"] | |
| def setup(self): | |
| if self.post_processor is None: | |
| self.post_processor = "microsoft/speecht5_hifigan" | |
| super().setup() | |
| def encode(self, text, speaker_embeddings=None): | |
| inputs = self.pre_processor(text=text, return_tensors="pt", truncation=True) | |
| if speaker_embeddings is None: | |
| if not is_datasets_available(): | |
| raise ImportError("Datasets needs to be installed if not passing speaker embeddings.") | |
| embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| speaker_embeddings = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0) | |
| return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings} | |
| def forward(self, inputs): | |
| with torch.no_grad(): | |
| return self.model.generate_speech(**inputs) | |
| def decode(self, outputs): | |
| with torch.no_grad(): | |
| return self.post_processor(outputs).cpu().detach() | |