Create app.py

app.py

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+import gradio as gr
+import librosa
+import numpy as np
+import soundfile as sf
+from transformers import pipeline
+
+asr_ceb = pipeline("automatic-speech-recognition", model = "sil-ai/wav2vec2-bloom-speech-ceb")
+asr_whisper_large = pipeline("automatic-speech-recognition", model = "openai/whisper-large-v3")
+asr_whisper_ceb = pipeline("automatic-speech-recognition",
+                           model = "nlewins/whisper-small-translate-X-gen2-examples-quality-step4-1e-6")
+
+
+def transcribe_speech(filepath):
+    if filepath is None:
+        gr.Warning("No audio found, please retry.")
+        return ""
+    _, sample_rate = librosa.load(filepath, sr = None)
+    model_rate = asr_ceb.feature_extractor.sampling_rate
+    if sample_rate != model_rate:
+        filepath = resample_audio_for_processing(filepath, model_rate, sample_rate)
+
+    output_ceb = asr_ceb(filepath)
+    generate_kwargs = {
+        # "language": "tagalog",#source language
+        "task": "translate"
+    }
+    output_whisper_large_translate = asr_whisper_large(filepath, generate_kwargs = generate_kwargs)
+    output_whisper_large = asr_whisper_large(filepath)
+    output_whisper_ceb = asr_whisper_ceb(filepath)
+    return (output_ceb["text"], output_whisper_large["text"], output_whisper_large_translate["text"],
+            output_whisper_ceb["text"])
+
+
+def resample_audio_for_processing(filepath, model_rate, sample_rate):
+    print(f"Audio loaded with rate: {sample_rate} Hz while model requires rate: {model_rate} Hz")
+    try:
+        print("Resampling audio...")
+        audio_data, sr = librosa.load(filepath, sr = None)  # Audio data will be a NumPy array
+        # Ensure that audio_data is a NumPy array
+        audio_data = np.array(audio_data)
+        # Resample to 16kHz
+        audio_resampled = librosa.resample(audio_data, orig_sr = sample_rate, target_sr = model_rate)
+        # Save the resampled audio
+        resampled_audio_path = 'resampled_audio.wav'
+        sf.write(resampled_audio_path, audio_resampled, 16000)
+        print("Audio resampled successfully.")
+        return resampled_audio_path
+    except Exception as e:
+        print(f"Error resampling audio: {e}, processing with audio as is it !")
+        return filepath
+
+
+mic_transcribe = gr.Interface(
+    fn = transcribe_speech,
+    inputs = gr.Audio(sources = ["microphone"], type = "filepath"),
+    outputs = [gr.Textbox(label = "Transcription CEB (sil-ai)"), gr.Textbox(label = "Transcription (openai)"),
+               gr.Textbox(label = "Translation (openai)"),
+               gr.Textbox(label = "Transcription (nlewins)")]
+    , allow_flagging = "never")
+
+file_transcribe = gr.Interface(
+    fn = transcribe_speech,
+    inputs = gr.Audio(sources = ["upload"], type = "filepath"),
+    outputs = [gr.Textbox(label = "Transcription CEB (sil-ai)"), gr.Textbox(label = "Transcription (openai)"),
+               gr.Textbox(label = "Translation (openai)"),
+               gr.Textbox(label = "Translation (nlewins)")]
+    , allow_flagging = "never",
+)
+
+demo = gr.TabbedInterface(
+    [mic_transcribe, file_transcribe],
+    ["Use your Microphone", "Upload Audio File"],
+)
+
+if __name__ == '__main__':
+    demo.launch()