import gradio as gr from transformers import pipeline import torch import librosa import json max_duration = int(30 * 16000) def load_model(model_name = "cawoylel/windanam_mms-1b-tts_v2"): """ Function to load model from hugging face """ pipe = pipeline("automatic-speech-recognition", model="cawoylel/windanam_mms-1b-tts_v2") return pipe pipeline = load_model() def transcribe_audio(sample): """ Transcribe audio """ transcription = pipeline(sample) return transcription["text"] 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) duration = librosa.get_duration(y=speech, sr=16000) if duration > 30: speech = speech[:max_duration] return transcribe_audio(speech) description = '''windanam-mms is a Multidialectal ASR model for Fula and base on the MMS speech model: [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516).''' 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"), description=description ) iface.launch()