File size: 2,177 Bytes
df7a6c1 0d8bca1 df7a6c1 b9b949f df7a6c1 0d8bca1 |
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 |
from pathlib import Path
import gradio as gr
import librosa
from transformers import (AutoFeatureExtractor,
AutoModelForAudioClassification, pipeline)
feature_extractor = AutoFeatureExtractor.from_pretrained(
"sanchit-gandhi/whisper-medium-fleurs-lang-id",
do_normalize=True
)
SR = 16_000
label2id = {
'blues': '0',
'classical': '1',
'country': '2',
'disco': '3',
'hiphop': '4',
'jazz': '5',
'metal': '6',
'pop': '7',
'reggae': '8',
'rock': '9'
}
num_labels = len(label2id)
path = "best_Whisper-Small_model_92"
model = AutoModelForAudioClassification.from_pretrained("allispaul/whisper-small-gtzan")
classifier = pipeline("audio-classification", model=model, feature_extractor=feature_extractor)
def predict(audio):
if isinstance(audio, str) or isinstance(audio, Path):
audio, sr = librosa.load(audio, sr=16_000)
else:
sr, audio = audio
preds = classifier({"array": audio, "sampling_rate": sr})
probs = {k: 0.0 for k in label2id}
for pred in preds:
probs[pred["label"]] = pred["score"]
return probs
title = "🤖 🎵 Audiobot 🎹 ⚡"
description = "<h2>♯ The superpowered music genre classifier ♭</h2>"
article = """
<p>🤖 This model is a version of <a href="https://huggingface.co/openai/whisper-medium">Whisper Small</a>,
fine-tuned on the <a href="https://huggingface.co/datasets/marsyas/gtzan">GTZAN</a> dataset.</p>
<p>🎹 It recognizes 10 genres: blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock.</p>
<p>🎵 Upload a song or click one of the examples to try it out!</p>
<p>⚡ Part of a project for the <a href="https://www.erdosinstitute.org/">Erdős Institute</a> Deep Learning bootcamp,
by Dylan Bates, Muhammed Cifci, Aycan Katitas, Johann Thiel, Soheyl Anbouhi, and Paul VanKoughnett.</p>
"""
iface = gr.Interface(
fn=predict,
inputs=gr.Audio(type="filepath"),
outputs=gr.Label(num_top_classes=3),
title=title,
description=description,
article=article,
examples=["examples/country.00008.wav", "examples/hiphop.00057.wav", "examples/gamblersblues.opus"]
)
iface.launch()
|