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| 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(type="filepath", label="Record Audio"), | |
| outputs=gr.Textbox(label="Transcription"), | |
| description=description | |
| ) | |
| iface.launch() |