File size: 3,177 Bytes
b3dff30 | 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 | import gradio as gr
from transformers import pipeline
import numpy as np
classifier = pipeline(
"audio-classification",
model="dima806/bird_sounds_classification",
device=-1,
)
# Get the full species list from the model config
SPECIES_LIST = sorted(set(
classifier.model.config.id2label.values()
))
def classify_bird(audio):
if audio is None:
return "Please upload or record an audio file."
sr, y = audio
# Convert to float32 and normalize
if y.dtype == np.int16:
y = y.astype(np.float32) / 32768.0
elif y.dtype == np.int32:
y = y.astype(np.float32) / 2147483648.0
elif y.dtype != np.float32:
y = y.astype(np.float32)
# If stereo, take first channel
if len(y.shape) > 1:
y = y[:, 0]
# Resample to 16kHz if needed (model expects 16kHz)
if sr != 16000:
# Simple resampling using numpy interpolation
duration = len(y) / sr
new_length = int(duration * 16000)
y = np.interp(
np.linspace(0, len(y) - 1, new_length),
np.arange(len(y)),
y,
)
sr = 16000
results = classifier({"sampling_rate": sr, "raw": y}, top_k=5)
# Format output
lines = []
for i, pred in enumerate(results, 1):
score = pred["score"]
label = pred["label"]
if i == 1 and score < 0.40:
lines.append("Not confident - this may not be a recognizable bird song,")
lines.append("or the species may not be in this model's training data.")
lines.append(f"Best guess: {label} ({score:.0%})")
lines.append("")
lines.append("Top 5 predictions:")
lines.append(f" 1. {label} - {score:.1%}")
continue
bar_length = int(score * 20)
bar = "#" * bar_length + "." * (20 - bar_length)
lines.append(f"{i}. {label}")
lines.append(f" {bar} {score:.1%}")
return "\n".join(lines)
demo = gr.Interface(
fn=classify_bird,
inputs=gr.Audio(
label="Upload or Record a Bird Song",
type="numpy",
),
outputs=gr.Textbox(label="Classification Results", lines=12),
title="Bird Song Classifier",
description=(
"Upload a bird song recording and this model will try to identify the species. "
"Uses dima806/bird_sounds_classification, a wav2vec2-based classifier trained on "
"50 bird species (mostly Tinamous, Guans, and Chachalacas - neotropical birds). "
"Best results with clean recordings of 3+ seconds.\n\n"
"Note: This model was trained on tropical/neotropical species. "
"It won't recognize common North American backyard birds like cardinals or robins. "
"That's a training data limitation, not an architecture limitation.\n\n"
"Try recordings from Xeno-Canto (https://xeno-canto.org/) - search for species like "
"Great Tinamou, Plain Chachalaca, or Crested Guan."
),
article=(
"### Species this model knows\n\n"
+ ", ".join(SPECIES_LIST)
+ "\n\n---\n*Riley's Space 2 - AI + Research Level 2*"
),
theme=gr.themes.Soft(),
)
demo.launch() |