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import whisper
import gradio as gr
# load model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium")
model.config.forced_decoder_ids = None
def transcribe(audio):
#time.sleep(3)
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
# decode the audio
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
return result.text
# gr.Interface(
# title = 'Talk to NP',
# fn=transcribe,
# inputs=[
# gr.inputs.Audio(source="microphone", type="filepath")
# ],
# outputs=[
# "textbox"
# ],
# live=True).launch()
def speech_to_text(speech):
text = asr(speech)["text"]
return text
def text_to_sentiment(text):
return classifier(text)[0]["label"]
demo = gr.Blocks()
with demo:
audio_file = gr.Audio(type="filepath")
text1 = gr.Textbox()
text2 = gr.Textbox()
b1 = gr.Button("Transcribe audio")
b2 = gr.Button("Summarize")
b1.click(transcribe, inputs=audio_file, outputs=text)
b2.click(text_to_sentiment, inputs=text1, outputs=text)
demo.launch()
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