Update app.py

app.py

@@ -1,40 +1,9 @@
 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"]],
+    "DrishtiSharma/wav2vec2-base-finetuned-sentiment-mesd-v11",
     title="Hindi ASR using Wav2Vec2-1B with LM",
     description="Built during Robust Speech Event",
     layout="horizontal",