File size: 4,827 Bytes
e41b3b7 964f43a 41b767d e41b3b7 | 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 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | import os
import tempfile
from typing import List, Tuple
import librosa
import numpy as np
import streamlit as st
import torch
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
# =========================
# Config
# =========================
SAMPLE_RATE = 16000
SEGMENT_SEC = 10
SEGMENT_LEN = SAMPLE_RATE * SEGMENT_SEC
TOP_K = 5
# Replace this with your actual Hugging Face model repo, for example:
# MODEL_REPO = "your-username/ast-messy-mashup"
# Try again
MODEL_REPO = os.getenv("MODEL_REPO", "22ds2000101/20260411_best_ast_model.pt")
LABELS = [
"blues",
"classical",
"country",
"disco",
"hiphop",
"jazz",
"metal",
"pop",
"reggae",
"rock",
]
@st.cache_resource(show_spinner=True)
def load_model_and_extractor():
feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_REPO)
model = AutoModelForAudioClassification.from_pretrained(MODEL_REPO)
model.eval()
return feature_extractor, model
def load_audio(uploaded_file) -> np.ndarray:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
tmp.write(uploaded_file.getbuffer())
tmp_path = tmp.name
try:
audio, _ = librosa.load(tmp_path, sr=SAMPLE_RATE, mono=True)
return audio.astype(np.float32)
finally:
if os.path.exists(tmp_path):
os.remove(tmp_path)
def get_segments(audio: np.ndarray, max_segments: int = 3) -> List[np.ndarray]:
"""
Create deterministic 10-second segments.
- If audio is short, pad once.
- If audio is long, take evenly spaced windows.
"""
if len(audio) <= SEGMENT_LEN:
padded = np.pad(audio, (0, SEGMENT_LEN - len(audio)))
return [padded.astype(np.float32)]
if max_segments <= 1:
return [audio[:SEGMENT_LEN].astype(np.float32)]
max_start = len(audio) - SEGMENT_LEN
starts = np.linspace(0, max_start, num=max_segments, dtype=int)
segments = [audio[s : s + SEGMENT_LEN].astype(np.float32) for s in starts]
return segments
def predict_audio(
audio: np.ndarray,
feature_extractor,
model,
max_segments: int = 3,
) -> Tuple[str, List[Tuple[str, float]]]:
segments = get_segments(audio, max_segments=max_segments)
probs_per_segment = []
with torch.no_grad():
for segment in segments:
inputs = feature_extractor(
segment,
sampling_rate=SAMPLE_RATE,
return_tensors="pt",
)
logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1).cpu().numpy()[0]
probs_per_segment.append(probs)
mean_probs = np.mean(np.stack(probs_per_segment), axis=0)
pred_idx = int(np.argmax(mean_probs))
predicted_label = LABELS[pred_idx]
ranked = sorted(
[(LABELS[i], float(mean_probs[i])) for i in range(len(LABELS))],
key=lambda x: x[1],
reverse=True,
)
return predicted_label, ranked[:TOP_K]
# =========================
# UI
# =========================
st.set_page_config(page_title="Messy Mashup Genre Classifier", page_icon="🎵", layout="centered")
st.title("🎵 Messy Mashup Genre Classifier")
st.markdown(
"Upload an audio file and the app will predict its genre using your fine-tuned "
"Audio Spectrogram Transformer model."
)
with st.expander("Model settings", expanded=False):
st.write(f"Model repo: `{MODEL_REPO}`")
st.write(f"Sample rate: `{SAMPLE_RATE}` Hz")
st.write(f"Segment length: `{SEGMENT_SEC}` seconds")
st.write("Inference uses up to 3 evenly spaced segments and averages class probabilities.")
uploaded_file = st.file_uploader(
"Upload audio",
type=["wav", "mp3", "flac", "ogg", "m4a"],
)
if MODEL_REPO == "your-username/your-model-repo":
st.warning(
"Set your model repo first. In the Space Settings, add an environment variable named "
"`MODEL_REPO`, or replace the default value inside `app.py`."
)
if uploaded_file is not None:
st.audio(uploaded_file)
try:
feature_extractor, model = load_model_and_extractor()
with st.spinner("Running inference..."):
audio = load_audio(uploaded_file)
predicted_label, top_predictions = predict_audio(audio, feature_extractor, model)
st.success(f"Predicted genre: **{predicted_label}**")
st.subheader("Top predictions")
for label, score in top_predictions:
st.progress(min(max(score, 0.0), 1.0), text=f"{label}: {score:.4f}")
except Exception as e:
st.error("The app could not complete inference.")
st.exception(e)
st.markdown("---")
st.caption(
"Tip: for deployment, upload the fine-tuned model to a Hugging Face model repository and point "
"this app to it with `MODEL_REPO`."
)
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