Upload app.py

app.py

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+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",
+    description="Built during Robust Speech Event",
+    layout="horizontal",
+    theme="huggingface",
+).launch(enable_queue=True)