Add app v0.1

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import torch
+import librosa
+import time
+import pandas as pd
+from datetime import datetime
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
+
+DESCRIPTION = "Store a record of previous calls in order to verify if the client already called or not. Pretrained on `https://huggingface.co/datasets/superb` using [S3PRL recipe](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1)."
+
+# COLUMNS = ["call_id", "date", "client_id", "duration", "new"]
+model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-large-superb-sid")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-large-superb-sid")
+
+def file_to_array(path):
+    speech, _ = librosa.load(path, sr=16000, mono=True)
+    duration = librosa.get_duration(y=speech)
+    return speech, duration
+
+
+def handler(audio_path):
+    calls = pd.read_csv("call_records.csv")
+    speech, duration = file_to_array(audio_path)
+
+    # compute attention masks and normalize the waveform if needed
+    inputs = feature_extractor(speech, sampling_rate=16000, padding=True, return_tensors="pt")
+
+    logits = model(**inputs).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
+
+    client_id = labels[0]    
+    call_id = str(int(time.time()))
+
+    date = datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+
+    n_of_calls = len(calls.loc[calls.client_id == client_id])
+    new = n_of_calls == 0
+
+    # add new call record 
+    record = [call_id, date, client_id, duration, new]
+    calls.loc[len(calls)] = record
+
+    calls.to_csv("call_records.csv", index=False)
+
+    if new:
+        return f"New client call: Client ID {client_id}"
+    
+    return f"Client {client_id} calling again: {n_of_calls} previous calls"
+    
+
+first = gr.Interface(
+    fn=handler,
+    inputs=gr.Audio(label="Speech Audio", type="filepath"),
+       
+    outputs=gr.Text(label="Output", value="..."),
+    description=DESCRIPTION
+
+)
+
+second = gr.Interface(
+    fn=handler,
+    inputs=gr.Audio(label="Microphone Input", source="microphone", type="filepath"),
+    outputs=gr.Text(label="Output", value="..."),
+    description=DESCRIPTION
+)
+
+app = gr.TabbedInterface(
+    [first, second],
+    title="Speaker Call Verification 🎤",
+    tab_names=["Audio Upload", "Microphone"],
+)
+app.launch()