Update app.py

app.py

@@ -22,10 +22,10 @@ def load_and_fix_data(input_file, model_sampling_rate):
     return speech
 
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
+feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-xls-r-1b-spanish")
 sampling_rate = feature_extractor.sampling_rate
 
-asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
+asr = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-xls-r-1b-spanish")
 
 prefix = ''
 model_checkpoint = "hackathon-pln-es/es_text_neutralizer"
@@ -116,7 +116,11 @@ def predict_and_ctc_lm_decode(input_file, speaker_idx: str=None):
         synthesizer.save_wav(wavs, fp)
         return fp.name
 
+description = """This is a Gradio demo for generating gender-neutralized audios. To use it, simply provide an audio input (via microphone or audio recording), which will then be transcribed and gender-neutralized using pre-trained models. Finally, with the help of Coqui's TTS model, gender neutralized audio is generated.
 
+> Pre-trained model used for Spanish ASR: [Please Click Here](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-spanish)
+>
+"""
 
 
 gr.Interface(
@@ -127,7 +131,7 @@ gr.Interface(
     outputs=gr.outputs.Audio(label="Output"),
     examples=[["Example1.wav"],["Example2.wav"],["Example3.wav"]],
     title="Generate-Gender-Neutralized-Audios",
-    description = "This is a Gradio demo for generating gender neutralized audios. To use it, simply provide an audio input (via microphone or audio recording), which will then be transcribed and gender-neutralized using pre-trained models. Finally, with the help of Coqui's TTS model, gender neutralised audio is generated.",
+    description = description,
     #article="<p><center><img src='........e'></center></p>",
     layout="horizontal",
     theme="huggingface",