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| import whisper | |
| import gradio as gr | |
| from transformers import pipeline | |
| model = whisper.load_model("base") | |
| sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english") | |
| def process_audio_file(file): | |
| with open(file, "rb") as f: | |
| inputs = f.read() | |
| audio = ffmpeg_read(inputs, sampling_rate) | |
| return audio | |
| def transcribe(Microphone, File_Upload): | |
| warn_output = "" | |
| if (Microphone is not None) and (File_Upload is not None): | |
| warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \ | |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| file = Microphone | |
| elif (Microphone is None) and (File_Upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| elif Microphone is not None: | |
| file = Microphone | |
| else: | |
| file = File_Upload | |
| result = model.transcribe(file, task="translate") | |
| text = sentiment_analysis(result['text']) | |
| label = text[0]['label'] | |
| score = text[0]['score'] | |
| return label, score | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type='filepath', optional=True), | |
| gr.inputs.Audio(source="upload", type='filepath', optional=True), | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label="Sentiment"), | |
| gr.outputs.Textbox(label="Score") | |
| ], | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="AzVoiceSent: Sentiment Classification from Voice Transcriptions in Azerbaijani", | |
| description="AzVoiceSent is research project focused on sentiment classification from voice transcriptions in Azerbaijani. The project has the potential to provide valuable insights into the sentiment expressed by speakers in various domains and applications. ", | |
| allow_flagging='never', | |
| ) | |
| iface.launch(enable_queue=True) |