!pip install fastapi uvicorn python-multipart librosa numpy ai-edge-litert pycloudflared nest-asyncio import numpy as np import uvicorn import librosa import io import threading import asyncio import shutil import os from fastapi import FastAPI, File, UploadFile from fastapi.responses import HTMLResponse, JSONResponse from fastapi.middleware.cors import CORSMiddleware from numpy.lib.stride_tricks import as_strided from typing import Tuple, Optional from ai_edge_litert.interpreter import Interpreter from pycloudflared import try_cloudflare from pydub import AudioSegment # ========================================== # 1. CORE LOGIC (GIỮ NGUYÊN) # ========================================== def mel_scale_scalar(freq: float) -> float: return 1127.0 * np.log(1.0 + freq / 700.0) def mel_scale(freq: np.ndarray) -> np.ndarray: return 1127.0 * np.log(1.0 + freq / 700.0) def inverse_mel_scale(mel: np.ndarray) -> np.ndarray: return 700.0 * (np.exp(mel / 1127.0) - 1.0) def get_mel_banks(num_bins, window_length_padded, sample_freq, low_freq, high_freq, vtln_low, vtln_high, vtln_warp_factor): assert num_bins > 3 assert window_length_padded % 2 == 0 num_fft_bins = window_length_padded // 2 nyquist = 0.5 * sample_freq if high_freq <= 0.0: high_freq += nyquist fft_bin_width = sample_freq / window_length_padded mel_low_freq = mel_scale_scalar(low_freq) mel_high_freq = mel_scale_scalar(high_freq) mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1) if vtln_high < 0.0: vtln_high += nyquist bin = np.arange(num_bins)[:, np.newaxis] left_mel = mel_low_freq + bin * mel_freq_delta center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta center_freqs = inverse_mel_scale(center_mel).squeeze(-1) mel = mel_scale(fft_bin_width * np.arange(num_fft_bins))[np.newaxis, :] up_slope = (mel - left_mel) / (center_mel - left_mel) down_slope = (right_mel - mel) / (right_mel - center_mel) bins = np.maximum(0.0, np.minimum(up_slope, down_slope)) return bins, center_freqs def stft(input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=True): if hop_length is None: hop_length = n_fft // 4 if win_length is None: win_length = n_fft if window is None: window = np.ones(win_length) if len(window) < n_fft: pad_width = (n_fft - len(window)) // 2 window = np.pad(window, (pad_width, n_fft - len(window) - pad_width)) input = np.asarray(input) if input.ndim == 1: input = input[np.newaxis, :] squeeze_batch = True else: squeeze_batch = False if center: pad_width = int(n_fft // 2) input = np.pad(input, ((0, 0), (pad_width, pad_width)), mode=pad_mode) n_frames = 1 + (input.shape[-1] - n_fft) // hop_length frame_length = n_fft frame_step = hop_length frame_stride = input.strides[-1] shape = (input.shape[0], n_frames, frame_length) strides = (input.strides[0], frame_step * frame_stride, frame_stride) frames = as_strided(input, shape=shape, strides=strides, writeable=False) frames = frames * window stft_matrix = np.fft.fft(frames, n=n_fft, axis=-1) if normalized: stft_matrix = stft_matrix / np.sqrt(n_fft) if onesided: stft_matrix = stft_matrix[..., :(n_fft // 2) + 1] result = stft_matrix if return_complex else np.stack((stft_matrix.real, stft_matrix.imag), axis=-1) if squeeze_batch: result = result[0] return result class MelSTFT: def __init__(self, n_mels=128, sr=32000, win_length=800, hopsize=320, n_fft=1024, fmin=0.0, fmax=None): self.n_mels = n_mels self.sr = sr self.win_length = win_length self.hopsize = hopsize self.n_fft = n_fft self.fmin = fmin self.fmax = fmax if fmax else sr // 2 - 1000 self.window = np.hanning(win_length) self.mel_basis, _ = get_mel_banks(self.n_mels, self.n_fft, self.sr, self.fmin, self.fmax, 100.0, -500., 1.0) self.mel_basis = np.pad(self.mel_basis, ((0, 0), (0, 1)), mode='constant', constant_values=0) self.preemphasis_coefficient = np.array([-.97, 1]).reshape(1, 1, 2) def preemphasis(self, x): x = x.reshape(1, 1, -1) output_size = x.shape[2] - self.preemphasis_coefficient.shape[2] + 1 result = np.zeros((1, 1, output_size)) for i in range(output_size): result[0, 0, i] = np.sum(x[0, 0, i:i+2] * self.preemphasis_coefficient[0, 0]) return result[0] def __call__(self, x): x = self.preemphasis(x) spec_x = stft(input=x, n_fft=self.n_fft, hop_length=self.hopsize, win_length=self.win_length, window=self.window, return_complex=False) spec_x = np.sum(spec_x ** 2, axis=-1) melspec = np.dot(self.mel_basis, spec_x.transpose(0,2,1)).transpose(1,0,2) melspec = np.log(melspec + 1e-5) melspec = (melspec + 4.5) / 5. return melspec def softmax(x): exp_x = np.exp(x - np.max(x)) return exp_x / np.sum(exp_x, axis=-1, keepdims=True) # ========================================== # 2. SETUP BACKEND # ========================================== app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], ) MODEL_PATH = '/content/emotion_model_2025_08_18212.tflite' interpreter = None input_details = None output_details = None model_lock = threading.Lock() mel_processor = MelSTFT(n_mels=128, sr=32000, win_length=800, hopsize=320) CLASSES = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise'] @app.on_event("startup") def load_model(): global interpreter, input_details, output_details try: interpreter = Interpreter(model_path=MODEL_PATH) interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() print("✅ Model loaded successfully!") except Exception as e: print(f"❌ Error loading model: {e}") # ========================================== # 3. FRONTEND INTERFACE (CÓ THÊM REPLAY) # ========================================== html_content = """ AI Emotion Detection

🎙️ Cảm xúc giọng nói

Hệ thống phân tích cảm xúc qua giọng nói (AI)

Sẵn sàng

🎧 Nghe lại giọng của bạn:

""" @app.get("/", response_class=HTMLResponse) async def home(): return html_content # ========================================== # 4. API PREDICT (ĐÃ FIX PYDUB CHO WEBM) # ========================================== @app.post("/predict") async def predict(file: UploadFile = File(...)): # Tên file tạm webm_filename = "temp_input.webm" wav_filename = "temp_converted.wav" try: # 1. Lưu file WebM gốc with open(webm_filename, "wb") as buffer: shutil.copyfileobj(file.file, buffer) # 2. Convert WebM -> WAV (Fix lỗi librosa) audio = AudioSegment.from_file(webm_filename) audio = audio.set_frame_rate(32000).set_channels(1) audio.export(wav_filename, format="wav") # 3. Librosa đọc waveform, _ = librosa.load(wav_filename, sr=32000, mono=True) except Exception as e: import traceback traceback.print_exc() return JSONResponse(status_code=500, content={"error": f"Lỗi xử lý file: {str(e)}"}) finally: # Dọn dẹp if os.path.exists(webm_filename): os.remove(webm_filename) if os.path.exists(wav_filename): os.remove(wav_filename) # 4. Preprocessing & Inference waveform = np.stack([waveform]) spec = mel_processor(waveform) target_len = 400 if spec.shape[-1] > target_len: spec = spec[:, :, :target_len] elif spec.shape[-1] < target_len: spec = np.pad(spec, ((0, 0), (0, 0), (0, target_len - spec.shape[-1])), mode='constant') spec = np.expand_dims(spec, axis=0).astype(np.float32) if interpreter is None: return JSONResponse(status_code=500, content={"error": "Model not loaded"}) with model_lock: interpreter.set_tensor(input_details[0]['index'], spec) interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) preds = softmax(output_data[0]) results = [] sorted_indexes = np.argsort(preds)[::-1] for k in range(len(CLASSES)): results.append({ "label": CLASSES[sorted_indexes[k]], "score": float(preds[sorted_indexes[k]]) }) return {"results": results} # ========================================== # 5. RUN SERVER (FIX COLAB) # ========================================== if __name__ == "__main__": import nest_asyncio import uvicorn from pycloudflared import try_cloudflare nest_asyncio.apply() print("🚀 Đang khởi động Cloudflare Tunnel...") tunnel_url = try_cloudflare(port=8000) print(f"🔗 PUBLIC URL CỦA BẠN: {tunnel_url.tunnel}") print("👉 Click link trên để truy cập Web App") config = uvicorn.Config(app, host="0.0.0.0", port=8000) server = uvicorn.Server(config) await server.serve()