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