usrbinenv python codingutf8 2023 the huggingface inc team 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 orglicenseslicense2 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 usr bin env python coding utf 8 2023 the huggingface inc team 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]