Spaces:
Runtime error
Runtime error
File size: 2,389 Bytes
8b843d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 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() |