deploy app

app.py

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+import gradio as gr
+import nemo.collections.asr as nemo_asr
+from pydub import AudioSegment
+import pyaudioconvert as pac
+
+
+hf_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(
+                model_name="mbazaNLP/stt_rw_sw_lg_conformer_ctc_large")
+
+def convert (audio):
+  file_name = audio.name
+  if file_name.endswith("mp3") or file_name.endswith("wav") or file_name.endswith("ogg"):
+    if file_name.endswith("mp3"):
+      sound = AudioSegment.from_mp3(audio.name)
+      sound.export(audio.name, format="wav")
+    elif file_name.endswith("ogg"):
+      sound = AudioSegment.from_ogg(audio.name)
+      sound.export(audio.name, format="wav")
+  else:
+    return False
+  pac.convert_wav_to_16bit_mono(audio.name,audio.name)
+  return True
+
+def transcribe(audio, audio_microphone):
+  audio = audio_microphone if audio_microphone else audio
+  if convert(audio)== False:
+    return "The format must be mp3,wav and ogg"
+  result= hf_model.transcribe([audio.name])
+  return result[0]
+gradio_ui = gr.Interface(
+    fn=transcribe,
+    title="East african languages Speech Recognition",
+    description="Upload an audio clip or record from browser using microphone, and let AI do the hard work of transcribing. The supported languages are Kinyarwanda, Swahili and Luganda",
+    inputs=[gr.inputs.Audio(label="Upload Audio File", type="file", optional=True), gr.inputs.Audio(source="microphone", type="file", optional=True, label="Record from microphone")],
+    outputs=[gr.outputs.Textbox(label="Recognized speech")]
+)
+gradio_ui.launch(enable_queue=True)

packages.txt

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+libsndfile1 
+ffmpeg 
+sox

requirements.txt

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+pydub 
+pyaudioconvert
+nemo_toolkit[asr]