Update app.py

app.py

@@ -19,23 +19,40 @@
 
 from speechbrain.inference.ASR import EncoderASR
 import gradio as gr
+import numpy as np
+import sounddevice as sd
+import soundfile as sf
 
 model = EncoderASR.from_hparams("speechbrain/asr-wav2vec2-dvoice-wolof")
 
-def transcribe(audio):
+def transcribe_audio(audio):
     return model.transcribe_file(audio.name)
 
-def transcribe_microphone(audio):
-    return model.transcribe_array(audio)
+def transcribe_microphone():
+    # Record audio from microphone for 5 seconds
+    duration = 60  # seconds
+    fs = 16000  # sampling rate
+    recording = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype=np.int16)
+    sd.wait()
+
+    # Save recorded audio to a temporary file
+    temp_audio_file = "temp_audio.wav"
+    sf.write(temp_audio_file, recording, fs)
+
+    # Transcribe the recorded audio
+    transcription = model.transcribe_file(temp_audio_file)
+
+    return transcription
 
 demo = gr.Interface(
-    fn=[transcribe, transcribe_microphone], 
-    inputs=["file", gr.inputs.Microphone(audio=False)], 
-    outputs="text", 
-    title="Transcription automatique du wolof", 
+    fn=[transcribe_audio, transcribe_microphone],
+    inputs=["file", "microphone"],
+    outputs="text",
+    title="Transcription automatique du wolof",
     description="Ce modèle transcrit un fichier audio en wolof en texte en utilisant l'alphabet latin.",
-    input_labels=["Audio en wolof", "Microphone (parlez en wolof)"], 
+    input_labels=["Audio en wolof", "Microphone (parlez en wolof)"],
     output_label="Transcription alphabet latin"
 )
 
 demo.launch()
+