Create app.py

app.py

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+import gradio as gr
+import time
+from faster_whisper import WhisperModel
+from utils import ffmpeg_read, stt, greeting_list
+from sentence_transformers import SentenceTransformer, util
+import torch
+
+whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"]
+audio_model = WhisperModel("base", compute_type="int8", device="cpu")
+text_model = SentenceTransformer('all-MiniLM-L6-v2')
+corpus_embeddings = torch.load('corpus_embeddings.pt')
+model_type = "whisper"
+
+def speech_to_text(upload_audio):
+    """
+    Transcribe audio using whisper model.
+    """
+    # Transcribe audio
+    if model_type == "whisper":
+        transcribe_options = dict(task="transcribe", language="ja", beam_size=5, best_of=5, vad_filter=True)
+        segments_raw, info = audio_model.transcribe(upload_audio, **transcribe_options)
+        segments = [segment.text for segment in segments_raw]
+        return ' '.join(segments)
+    else:
+        text = stt(upload_audio)
+        return text
+
+def voice_detect(audio, recongnize_text=""):
+    """
+    Transcribe audio using whisper model.
+    """
+    time.sleep(2)
+    if len(recongnize_text) !=0:
+        count_state = int(recongnize_text[0])
+        recongnize_text = recongnize_text[1:]
+    else:
+        count_state = 0
+
+    threshold = 0.8
+    detect_greeting = 0
+    text = speech_to_text(audio)
+    recongnize_text = recongnize_text + " " + text
+    query_embedding = text_model.encode(text, convert_to_tensor=True)
+    for greeting in greeting_list:
+        if greeting in text:
+            detect_greeting = 1
+            break
+    if detect_greeting == 0:
+        hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=1)[0]
+        if hits[0]['score'] > threshold:
+            detect_greeting = 1
+
+    recongnize_state = str(count_state + detect_greeting) + recongnize_text
+    return  recongnize_text, recongnize_state, count_state
+
+demo = gr.Interface(
+    title= "Greeting detection demo app",
+    fn=voice_detect, 
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", streaming=True), 
+        "state", 
+    ],
+    outputs=[
+        gr.Textbox(label="Predicted"),
+        "state",
+        gr.Number(label="Greeting count"),
+    ],
+    live=True)
+
+demo.launch(debug=True)