40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 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 .tools import PipelineTool
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class SpeechToTextTool(PipelineTool):
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default_checkpoint = "distil-whisper/distil-large-v3"
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description = "This is a tool that transcribes an audio into text. It returns the transcribed text."
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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": {"type": "audio", "description": "The audio to transcribe"}}
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output_type = "string"
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def encode(self, audio):
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return self.pre_processor(audio, return_tensors="pt")
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def forward(self, inputs):
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return self.model.generate(inputs["input_features"])
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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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