| import gradio as gr | |
| import librosa | |
| from transformers import AutoFeatureExtractor, pipeline | |
| def load_and_fix_data(input_file, model_sampling_rate): | |
| speech, sample_rate = librosa.load(input_file) | |
| if len(speech.shape) > 1: | |
| speech = speech[:, 0] + speech[:, 1] | |
| if sample_rate != model_sampling_rate: | |
| speech = librosa.resample(speech, sample_rate, model_sampling_rate) | |
| return speech | |
| feature_extractor = AutoFeatureExtractor.from_pretrained( | |
| "anuragshas/wav2vec2-xls-r-1b-hi-with-lm" | |
| ) | |
| sampling_rate = feature_extractor.sampling_rate | |
| asr = pipeline( | |
| "automatic-speech-recognition", model="anuragshas/wav2vec2-xls-r-1b-hi-with-lm" | |
| ) | |
| def predict_and_ctc_lm_decode(input_file): | |
| speech = load_and_fix_data(input_file, sampling_rate) | |
| transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1) | |
| return transcribed_text["text"] | |
| gr.Interface( | |
| predict_and_ctc_lm_decode, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio") | |
| ], | |
| outputs=[gr.outputs.Textbox()], | |
| examples=[["example1.wav"]], | |
| title="Hindi ASR using Wav2Vec2-1B with LM", | |
| article="<p><center><img src='https://visitor-badge.glitch.me/badge?page_id=anuragshas/Hindi_ASR' alt='visitor badge'></center></p>", | |
| description="Built during Robust Speech Event", | |
| layout="horizontal", | |
| theme="huggingface", | |
| ).launch(enable_queue=True, cache_examples=True) | |