42 lines
1.4 KiB
Python
42 lines
1.4 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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from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
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from .base import PipelineTool
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class SpeechToTextTool(PipelineTool):
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default_checkpoint = "openai/whisper-base"
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description = (
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"This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
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"transcribed text."
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)
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name = "transcriber"
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pre_processor_class = WhisperProcessor
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model_class = WhisperForConditionalGeneration
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inputs = ["audio"]
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outputs = ["text"]
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def encode(self, audio):
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return self.pre_processor(audio, return_tensors="pt").input_features
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def forward(self, inputs):
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return self.model.generate(inputs=inputs)
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def decode(self, outputs):
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return self.pre_processor.batch_decode(outputs, skip_special_tokens=True)[0]
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