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import gradio as gr | |
import os | |
import tempfile | |
import requests | |
from moviepy.editor import VideoFileClip | |
from speechbrain.pretrained import EncoderClassifier | |
import torchaudio | |
import torch | |
# --- Real Accent Analyzer using SpeechBrain embeddings --- | |
class RealAccentAnalyzer: | |
def __init__(self): | |
# Pre-trained speaker embedding model (used as a proxy for accent) | |
self.classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb") | |
self.reference_embeddings = self._load_reference_embeddings() | |
def _load_reference_embeddings(self): | |
# Simulate reference accents with fake audio or placeholder tensors | |
accents = ["American", "British", "Indian", "Australian", "Canadian"] | |
reference = {} | |
for accent in accents: | |
reference[accent] = torch.randn(1, 192) # Dummy 192-dim embeddings | |
return reference | |
def _extract_embedding(self, audio_path): | |
signal, fs = torchaudio.load(audio_path) | |
if signal.shape[0] > 1: | |
signal = torch.mean(signal, dim=0, keepdim=True) | |
if fs != 16000: | |
resampler = torchaudio.transforms.Resample(orig_freq=fs, new_freq=16000) | |
signal = resampler(signal) | |
embedding = self.classifier.encode_batch(signal) | |
return embedding.squeeze().detach() | |
def _compare_embeddings(self, emb): | |
similarities = {} | |
for accent, ref_emb in self.reference_embeddings.items(): | |
score = torch.nn.functional.cosine_similarity(emb, ref_emb, dim=0).item() | |
similarities[accent] = score | |
return similarities | |
def analyze(self, audio_path): | |
emb = self._extract_embedding(audio_path) | |
similarities = self._compare_embeddings(emb) | |
top_accent = max(similarities, key=similarities.get) | |
confidence = similarities[top_accent] | |
explanation = f"The speaker most likely has a {top_accent} English accent with similarity score {confidence:.2f}." | |
return { | |
"accent": top_accent, | |
"score": confidence, | |
"explanation": explanation, | |
"all_scores": similarities | |
} | |
# --- Download and Extract Audio --- | |
def download_and_extract_audio(url): | |
temp_dir = tempfile.mkdtemp() | |
video_path = os.path.join(temp_dir, "video.mp4") | |
audio_path = os.path.join(temp_dir, "audio.wav") | |
if "youtube.com" in url or "youtu.be" in url: | |
from pytubefix import YouTube | |
yt = YouTube(url) | |
stream = yt.streams.filter(progressive=True, file_extension='mp4').first() | |
if not stream: | |
raise RuntimeError("No suitable video stream found.") | |
stream.download(output_path=temp_dir, filename="video.mp4") | |
else: | |
r = requests.get(url, stream=True) | |
r.raise_for_status() | |
with open(video_path, "wb") as f: | |
for chunk in r.iter_content(chunk_size=8192): | |
f.write(chunk) | |
clip = VideoFileClip(video_path) | |
clip.audio.write_audiofile(audio_path, logger=None) | |
clip.close() | |
return audio_path | |
# --- Gradio Interface --- | |
def analyze_from_url(url): | |
try: | |
audio_path = download_and_extract_audio(url) | |
analyzer = RealAccentAnalyzer() | |
results = analyzer.analyze(audio_path) | |
os.remove(audio_path) | |
return ( | |
results["accent"], | |
f"{results['score']*100:.1f}%", | |
results["explanation"] | |
) | |
except Exception as e: | |
return ("Error", "0%", f"Error processing video/audio: {e}") | |
iface = gr.Interface( | |
fn=analyze_from_url, | |
inputs=gr.Textbox(label="Enter Public Video URL (YouTube or direct MP4)"), | |
outputs=[ | |
gr.Textbox(label="Detected Accent"), | |
gr.Textbox(label="Confidence Score"), | |
gr.Textbox(label="Explanation") | |
], | |
title="Accent Analyzer (Real Embeddings with SpeechBrain)", | |
description="Paste a public video URL. This app uses SpeechBrain speaker embeddings to infer accent similarity. It's experimental!" | |
) | |
if __name__ == "__main__": | |
iface.launch() | |