66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from ..models.speecht5 import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor
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from ..utils import is_datasets_available
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from .base import PipelineTool
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if is_datasets_available():
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from datasets import load_dataset
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class TextToSpeechTool(PipelineTool):
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default_checkpoint = "microsoft/speecht5_tts"
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description = (
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"This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
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"text to read (in English) and returns a waveform object containing the sound."
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)
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name = "text_reader"
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pre_processor_class = SpeechT5Processor
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model_class = SpeechT5ForTextToSpeech
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post_processor_class = SpeechT5HifiGan
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inputs = ["text"]
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outputs = ["audio"]
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def setup(self):
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if self.post_processor is None:
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self.post_processor = "microsoft/speecht5_hifigan"
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super().setup()
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def encode(self, text, speaker_embeddings=None):
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inputs = self.pre_processor(text=text, return_tensors="pt", truncation=True)
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if speaker_embeddings is None:
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if not is_datasets_available():
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raise ImportError("Datasets needs to be installed if not passing speaker embeddings.")
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embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
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speaker_embeddings = torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)
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return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
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def forward(self, inputs):
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with torch.no_grad():
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return self.model.generate_speech(**inputs)
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def decode(self, outputs):
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with torch.no_grad():
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return self.post_processor(outputs).cpu().detach()
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