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add transformers_4_35_0

1ce5e18 7 months ago

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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]