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| from transformers import pipeline | |
| import gradio as gr | |
| import numpy as np | |
| import librosa | |
| # Initialize the speech recognition pipeline | |
| pipe = pipeline("automatic-speech-recognition", model="oyemade/w2v-bert-2.0-yoruba-CV17.0") | |
| def transcribe(audio): | |
| if audio is None: | |
| return "No audio detected. Please try again." | |
| try: | |
| # Check if the input is a file path (for uploaded files) or numpy array (for microphone input) | |
| if isinstance(audio, str): | |
| # Load the audio file using librosa | |
| audio, sr = librosa.load(audio, sr=16000) # Resample to 16kHz | |
| elif isinstance(audio, tuple): # Gradio audio components return a tuple (sr, audio) | |
| sr, audio = audio | |
| if sr != 16000: | |
| audio = librosa.resample(audio, sr, 16000) | |
| else: | |
| return "Invalid audio format. Please try again." | |
| # Check if the audio is valid (not silent) | |
| if np.max(np.abs(audio)) < 0.01: | |
| return "Audio is too quiet. Please speak louder or choose a different file and try again." | |
| text = pipe(audio)["text"] | |
| return text | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(sources=["microphone", "upload"], type="filepath", label="Audio Input"), | |
| ], | |
| outputs="text", | |
| title="Neoform AI: Yoruba Speech Recognition", | |
| description="Realtime demo for Yoruba speech recognition using a fine-tuned Wav2Vec-Bert model. " | |
| "You can either use your microphone or upload an MP3 file. " | |
| "https://neoformai.com", | |
| ) | |
| # Launch the interface | |
| iface.launch() |