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| import gradio as gr | |
| import torch | |
| import librosa | |
| import json | |
| from transformers import pipeline | |
| from stitched_model import CombinedModel | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model = CombinedModel("ak3ra/wav2vec2-sunbird-speech-lugbara", "Sunbird/sunbird-mul-en-mbart-merged", device="cpu") | |
| def transcribe(audio_file_mic=None, audio_file_upload=None): | |
| if audio_file_mic: | |
| audio_file = audio_file_mic | |
| elif audio_file_upload: | |
| audio_file = audio_file_upload | |
| else: | |
| return "Please upload an audio file or record one" | |
| # Make sure audio is 16kHz | |
| speech, sample_rate = librosa.load(audio_file) | |
| if sample_rate != 16000: | |
| speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000) | |
| speech = torch.tensor([speech]) | |
| with torch.no_grad(): | |
| transcription, translation = model({"audio":speech}) | |
| return transcription, translation[0] | |
| description = '''Luganda to English Speech Translation''' | |
| iface = gr.Interface(fn=transcribe, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", label="Record Audio"), | |
| gr.Audio(source="upload", type="filepath", label="Upload Audio")], | |
| outputs=[gr.Textbox(label="Transcription"), | |
| gr.Textbox(label="Translation") | |
| ], | |
| description=description | |
| ) | |
| iface.launch() |