""" Build AMD dataset v3: Mix real human speech + TTS-generated samples. Real speech sources: - PolyAI/minds14 (en-US, en-GB, en-AU): Real callers to phone banking IVR - MLCommons/peoples_speech (clean subset): Diverse conversational English Strategy: - human: ~200 real (MINDS14+peoples_speech) + ~200 TTS = 400 total - voicemail: 400 TTS (voicemail content IS typically human-recorded, TTS voices + beep) - ivr: 400 TTS (IVR systems ARE synthetic — TTS is realistic for this class) - answering_machine: 400 TTS (carrier messages ARE automated — TTS is realistic) This ensures the model can't shortcut by detecting "TTS signature = not human" since TTS appears in ALL classes, and real speech appears in the human class. """ import asyncio import edge_tts import numpy as np import soundfile as sf import os import sys import random from pydub import AudioSegment from scipy.signal import butter, lfilter from datasets import load_dataset, Audio, Dataset, ClassLabel random.seed(42) np.random.seed(42) sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1) SAMPLE_RATE = 16000 MAX_LENGTH_S = 10.0 OUTPUT_DIR = "/app/amd_dataset_v3" audio_dir = os.path.join(OUTPUT_DIR, "audio") os.makedirs(audio_dir, exist_ok=True) LABELS = ["human", "voicemail", "ivr", "answering_machine"] # ============ Names / Companies ============ NAMES = ["John", "Sarah", "Mike", "Emily", "David", "Lisa", "James", "Anna", "Robert", "Jessica", "Chris", "Rachel", "Tom", "Maria", "Steve", "Amy", "Kevin", "Sophia", "Daniel", "Nicole", "Mark", "Laura", "Brian", "Megan"] COMPANIES = ["Acme Corp", "Global Services", "Tech Solutions", "First National Bank", "City Medical Center", "Pacific Insurance", "Metro Legal", "Summit Realty", "Valley Dental", "Premier Auto", "Coastal Roofing", "Downtown Pizza"] # ============ Templates ============ HUMAN_TEMPLATES = [ "Hello?", "Hi there.", "Yeah?", "Hello, who's calling?", "Hi, this is {name} speaking.", "Hello, {name} here.", "Yeah hi, who is this?", "Hello, how can I help you?", "Hi, what can I do for you?", "Hey, what's up?", "Hello? Can you hear me?", "Yeah, go ahead.", "Hello, {name} speaking, how can I help?", "Good morning, this is {name}.", "Good afternoon, how may I direct your call?", "Hey, sorry I was just about to call you back.", "Hello? Oh hi, yes this is {name}.", "Yeah I'm here, go ahead.", "Hi, sorry about that, I'm here now.", "Hello, {company}, {name} speaking.", "Good morning, {company}, how can I help you today?", "Yes, speaking.", "This is {name}, who am I speaking with?", "Hello? Hold on one second please.", "Yeah hi, sorry I was in a meeting.", "Hi, thanks for calling back.", "Hello, yes I can talk right now.", "Sure, what do you need?", "Hi, is this about the appointment?", "Hello, what time works for you?", "Yeah, let me check on that for you.", "OK sure, I can help with that.", "Hello? Sorry, bad connection. Can you repeat that?", "Hey yeah, one moment please.", "Hi, I was expecting your call.", ] VOICEMAIL_TEMPLATES = [ "Hi, you've reached {name}. I'm not available right now. Please leave a message after the beep.", "Hey, it's {name}. I can't come to the phone right now. Leave me a message and I'll call you back.", "You've reached {name} at {company}. I'm away from my desk. Please leave your name, number, and a brief message.", "Hi, this is {name}. Sorry I missed your call. Please leave a message and I'll get back to you as soon as possible.", "Hello, you've reached the voicemail of {name}. I'm unable to take your call right now. Please leave a message.", "Hi, you've reached {name}. I'm either on another call or away from my phone. Please leave a detailed message.", "Hey, this is {name}'s phone. I can't answer right now. Leave a message after the tone.", "You've reached {name}. I'm out of the office right now. Leave a message or try again later.", "Hi, this is {name} with {company}. I'm not available to take your call. Please leave a message.", "Hello, you've reached {name}'s voicemail. Please leave your name and number and I'll return your call.", "Hi, it's {name}. I'm currently unavailable. Please leave a message after the beep and I'll get back to you.", "You've reached {name} at {company}. Our office hours are Monday through Friday, 9 to 5. Please leave a message.", "Hello, this is {name}. I'm not able to answer the phone right now. Your call is important to me. Please leave a message.", "Hey, you've reached {name}. Sorry I couldn't pick up. Drop me a message and I'll hit you back.", "Hi, this is the voicemail of {name}. Leave a message at the beep.", "You've reached {name}. I'm probably busy or in a meeting. Leave a message after the tone.", "Hello, you've reached {name}'s phone. I can't take your call right now. Please leave a message after the beep.", "Hi there, this is {name}. I'm currently away but your call is important to me. Please leave a message.", ] IVR_TEMPLATES = [ "Thank you for calling {company}. Press 1 for sales, press 2 for support, or press 0 for the operator.", "Welcome to {company}. For English, press 1. Para español, oprima el 2.", "Thank you for calling. Please listen carefully as our menu options have changed.", "Press 1 for billing inquiries. Press 2 for technical support. Press 3 for new accounts.", "Your call is important to us. Please stay on the line and your call will be answered in the order it was received.", "Thank you for calling {company}. Our business hours are Monday through Friday, 8 AM to 6 PM.", "Welcome. For account information, press 1. To make a payment, press 2. To report a problem, press 3.", "Thank you for calling {company} customer service. If you know your party's extension, you may dial it at any time.", "Hello, and thank you for calling {company}. Your call may be recorded for quality assurance purposes.", "Please hold while we transfer your call. Your estimated wait time is 5 minutes.", "All of our representatives are currently busy. Please continue to hold or press 1 to leave a callback number.", "For billing and payments, press 1. For appointments, press 2. For prescription refills, press 3. For all other inquiries, press 4.", "Welcome to {company} automated phone system. Please say the name of the person or department you are trying to reach.", "This call may be monitored or recorded for training purposes. Press 1 to continue.", "Thank you for calling. To access your account, please enter your account number followed by the pound sign.", "Welcome to {company}. For hours and locations, press 1. For customer service, press 2. To hear this menu again, press 9.", ] AM_TEMPLATES = [ "The person you are calling is not available. Please leave a message after the tone.", "The number you have dialed is not in service. Please check the number and dial again.", "The mailbox is full. Please try your call again later.", "The subscriber you have dialed is not available. At the tone, please record your message.", "This is an automated message. The person you are trying to reach is unavailable.", "Your call has been forwarded to an automated voice messaging system. Please leave a message after the tone.", "The wireless customer you are calling is not available. Please leave a message.", "We're sorry, but the person you called has a voice mailbox that has not been set up yet.", "The number you have reached is not in service at this time.", "At the tone, please record your message. When you are finished, hang up or press 1 for more options.", "Your call cannot be completed as dialed. Please check the number and dial again.", "The Google subscriber you have called is not available. Please leave your message after the tone.", ] # ============ Voice configs ============ HUMAN_VOICES = [ ("en-US-AvaNeural", "+5%", "+5Hz"), ("en-US-AndrewNeural", "+0%", "+0Hz"), ("en-US-EmmaNeural", "+8%", "+10Hz"), ("en-US-BrianNeural", "+5%", "+5Hz"), ("en-GB-SoniaNeural", "+0%", "+0Hz"), ("en-US-JennyNeural", "+3%", "+5Hz"), ("en-US-GuyNeural", "+5%", "+0Hz"), ("en-GB-RyanNeural", "+0%", "+0Hz"), ("en-AU-NatashaNeural", "+5%", "+5Hz"), ("en-AU-WilliamNeural", "+0%", "+0Hz"), ] VOICEMAIL_VOICES = [ ("en-US-JennyNeural", "-5%", "+0Hz"), ("en-US-AndrewNeural", "-5%", "-10Hz"), ("en-US-MichelleNeural", "-8%", "+0Hz"), ("en-US-EricNeural", "-5%", "+0Hz"), ("en-US-AvaNeural", "-3%", "+0Hz"), ("en-GB-SoniaNeural", "-5%", "+0Hz"), ("en-AU-NatashaNeural", "-5%", "+0Hz"), ("en-US-BrianNeural", "-5%", "-5Hz"), ] IVR_VOICES = [ ("en-US-ChristopherNeural", "-20%", "-50Hz"), ("en-US-GuyNeural", "-20%", "-40Hz"), ("en-US-SteffanNeural", "-25%", "-60Hz"), ("en-US-AriaNeural", "-20%", "-30Hz"), ("en-GB-RyanNeural", "-20%", "-40Hz"), ("en-US-EricNeural", "-15%", "-30Hz"), ] AM_VOICES = [ ("en-US-GuyNeural", "-15%", "-20Hz"), ("en-US-RogerNeural", "-10%", "-15Hz"), ("en-US-ChristopherNeural", "-15%", "-25Hz"), ("en-US-SteffanNeural", "-20%", "-40Hz"), ] # ============ Audio utils ============ def telephone_bandpass(audio, sr=16000, lowcut=300, highcut=3400): nyq = sr / 2 b, a = butter(4, [lowcut / nyq, min(highcut / nyq, 0.99)], btype='band') return lfilter(b, a, audio).astype(np.float32) def add_noise(audio, snr_db=25): p = np.mean(audio ** 2) + 1e-10 return (audio + np.random.normal(0, np.sqrt(p / (10 ** (snr_db / 10))), len(audio))).astype(np.float32) def generate_tone(freq, dur, sr=16000, amp=0.3): t = np.arange(int(dur * sr)) / sr return (amp * np.sin(2 * np.pi * freq * t)).astype(np.float32) def generate_dtmf(digit, dur=0.15, sr=16000, amp=0.15): dtmf = {'1':(697,1209),'2':(697,1336),'3':(697,1477),'4':(770,1209), '5':(770,1336),'6':(770,1477),'7':(852,1209),'8':(852,1336), '9':(852,1477),'0':(941,1336)} f1, f2 = dtmf.get(digit, (697, 1209)) t = np.arange(int(dur * sr)) / sr return (amp * (np.sin(2*np.pi*f1*t) + np.sin(2*np.pi*f2*t))).astype(np.float32) def silence(dur, sr=16000): return np.random.normal(0, 0.001, int(dur * sr)).astype(np.float32) def telephony_fx(audio, sr=16000): audio = telephone_bandpass(audio, sr) audio = add_noise(audio, snr_db=np.random.uniform(18, 35)) audio = audio * np.random.uniform(0.6, 1.3) return np.clip(audio, -1, 1).astype(np.float32) def load_mp3_wav(path, sr=16000): a = AudioSegment.from_mp3(path).set_frame_rate(sr).set_channels(1).set_sample_width(2) return np.array(a.get_array_of_samples(), dtype=np.float32) / 32768.0 def fill(t): return t.format(name=random.choice(NAMES), company=random.choice(COMPANIES)) # ============ Load real human speech ============ def decode_audio_bytes(audio_bytes, target_sr=16000): """Decode audio bytes using ffmpeg binary (works without system ffmpeg libs).""" import subprocess proc = subprocess.run( ['ffmpeg', '-i', 'pipe:0', '-f', 's16le', '-ar', str(target_sr), '-ac', '1', 'pipe:1'], input=audio_bytes, capture_output=True ) if proc.returncode == 0 and len(proc.stdout) > 0: return np.frombuffer(proc.stdout, dtype=np.int16).astype(np.float32) / 32768.0 return None def load_real_human_speech(n_target=200): """Load real human speech from MINDS14 via direct parquet + ffmpeg decoding.""" from huggingface_hub import hf_hub_download import pyarrow.parquet as pq samples = [] max_s = int(MAX_LENGTH_S * SAMPLE_RATE) # Source: PolyAI/minds14 — real phone callers to banking IVR parquet_files = { 'en-US': 'en-US/train-00000-of-00001.parquet', 'en-GB': 'en-GB/train-00000-of-00001.parquet', 'en-AU': 'en-AU/train-00000-of-00001.parquet', } for lang, pq_path in parquet_files.items(): if len(samples) >= n_target: break try: print(f" Loading MINDS14 {lang}...") local = hf_hub_download('PolyAI/minds14', pq_path, repo_type='dataset') table = pq.read_table(local) audio_col = table.column('audio') for i in range(len(audio_col)): if len(samples) >= n_target: break row = audio_col[i].as_py() audio_bytes = row.get('bytes') if not audio_bytes: continue arr = decode_audio_bytes(audio_bytes, SAMPLE_RATE) if arr is None or len(arr) < SAMPLE_RATE: # skip <1s continue if len(arr) > max_s: start = random.randint(0, len(arr) - max_s) arr = arr[start:start + max_s] # Light noise (already telephony audio) arr = add_noise(arr, snr_db=np.random.uniform(22, 35)) samples.append(arr) print(f" {lang}: {len(table)} rows available, total so far: {len(samples)}") except Exception as e: print(f" {lang} failed: {e}") print(f" Total real human speech: {len(samples)}") return samples[:n_target] # ============ TTS generation ============ async def gen_tts(text, voice, rate, pitch, wav_path): mp3 = wav_path.replace('.wav', '.mp3') await edge_tts.Communicate(text, voice=voice, rate=rate, pitch=pitch).save(mp3) audio = load_mp3_wav(mp3, SAMPLE_RATE) max_s = int(MAX_LENGTH_S * SAMPLE_RATE) audio = audio[:max_s] if len(audio) > max_s else audio audio = telephony_fx(audio, SAMPLE_RATE) sf.write(wav_path, audio, SAMPLE_RATE) os.remove(mp3) return audio # ============ Main ============ async def main(): N_REAL_HUMAN = 200 N_TTS_HUMAN = 200 N_VOICEMAIL = 400 N_IVR = 400 N_AM = 400 all_paths = [] all_labels = [] # ---- HUMAN: real speech + TTS ---- print("\n=== HUMAN class ===") print(f"Target: {N_REAL_HUMAN} real + {N_TTS_HUMAN} TTS = {N_REAL_HUMAN + N_TTS_HUMAN}") real_samples = load_real_human_speech(N_REAL_HUMAN) for i, arr in enumerate(real_samples): p = os.path.join(audio_dir, f"human_real_{i:05d}.wav") sf.write(p, arr, SAMPLE_RATE) all_paths.append(p) all_labels.append(0) print(f" Saved {len(real_samples)} real human samples") print(f" Generating {N_TTS_HUMAN} TTS human samples...") for i in range(N_TTS_HUMAN): text = fill(random.choice(HUMAN_TEMPLATES)) voice, rate, pitch = random.choice(HUMAN_VOICES) p = os.path.join(audio_dir, f"human_tts_{i:05d}.wav") try: await gen_tts(text, voice, rate, pitch, p) all_paths.append(p) all_labels.append(0) except Exception as e: print(f" human_tts_{i} failed: {e}") if (i + 1) % 50 == 0: print(f" TTS human: {i + 1}/{N_TTS_HUMAN}") # ---- VOICEMAIL ---- print(f"\n=== VOICEMAIL class ({N_VOICEMAIL} TTS) ===") for i in range(N_VOICEMAIL): text = fill(random.choice(VOICEMAIL_TEMPLATES)) voice, rate, pitch = random.choice(VOICEMAIL_VOICES) p = os.path.join(audio_dir, f"voicemail_{i:05d}.wav") try: await gen_tts(text, voice, rate, pitch, p) # Add beep audio, sr = sf.read(p) beep_freq = random.choice([440, 480, 620, 880, 1000]) beep = generate_tone(beep_freq, np.random.uniform(0.3, 0.8), sr, 0.35) fade = min(int(0.01 * sr), len(beep) // 4) beep[:fade] *= np.linspace(0, 1, fade) beep[-fade:] *= np.linspace(1, 0, fade) audio = np.concatenate([audio, silence(np.random.uniform(0.3, 0.8), sr), beep, silence(np.random.uniform(0.5, 1.0), sr)]) sf.write(p, audio[:int(MAX_LENGTH_S * sr)], sr) all_paths.append(p) all_labels.append(1) except Exception as e: print(f" voicemail_{i} failed: {e}") if (i + 1) % 50 == 0: print(f" Voicemail: {i + 1}/{N_VOICEMAIL}") # ---- IVR ---- print(f"\n=== IVR class ({N_IVR} TTS) ===") for i in range(N_IVR): text = fill(random.choice(IVR_TEMPLATES)) voice, rate, pitch = random.choice(IVR_VOICES) p = os.path.join(audio_dir, f"ivr_{i:05d}.wav") try: await gen_tts(text, voice, rate, pitch, p) # 30% chance: add DTMF response if random.random() < 0.3: audio, sr = sf.read(p) dtmf = generate_dtmf(random.choice(list('1234567890')), 0.15, sr) audio = np.concatenate([audio, silence(np.random.uniform(0.5, 1.5), sr), dtmf, silence(0.3, sr)]) sf.write(p, audio[:int(MAX_LENGTH_S * sr)], sr) all_paths.append(p) all_labels.append(2) except Exception as e: print(f" ivr_{i} failed: {e}") if (i + 1) % 50 == 0: print(f" IVR: {i + 1}/{N_IVR}") # ---- ANSWERING MACHINE ---- print(f"\n=== ANSWERING MACHINE class ({N_AM} TTS) ===") for i in range(N_AM): text = fill(random.choice(AM_TEMPLATES)) voice, rate, pitch = random.choice(AM_VOICES) p = os.path.join(audio_dir, f"am_{i:05d}.wav") try: await gen_tts(text, voice, rate, pitch, p) # Add AM beep audio, sr = sf.read(p) beep_freq = random.choice([1000, 1200, 1400]) beep = generate_tone(beep_freq, np.random.uniform(0.8, 1.5), sr, 0.45) fade = int(0.02 * sr) beep[:fade] *= np.linspace(0, 1, fade) beep[-fade:] *= np.linspace(1, 0, fade) audio = np.concatenate([audio, silence(np.random.uniform(0.2, 0.5), sr), beep, silence(np.random.uniform(1.0, 2.0), sr)]) sf.write(p, audio[:int(MAX_LENGTH_S * sr)], sr) all_paths.append(p) all_labels.append(3) except Exception as e: print(f" am_{i} failed: {e}") if (i + 1) % 50 == 0: print(f" AM: {i + 1}/{N_AM}") # ---- Build dataset ---- print(f"\n=== Building dataset: {len(all_paths)} samples ===") for i, name in enumerate(LABELS): c = sum(1 for l in all_labels if l == i) print(f" {name}: {c}") dataset = Dataset.from_dict({"audio": all_paths, "label": all_labels}) dataset = dataset.cast_column("audio", Audio(sampling_rate=SAMPLE_RATE)) dataset = dataset.cast_column("label", ClassLabel(names=LABELS)) dataset = dataset.shuffle(seed=42) splits = dataset.train_test_split(test_size=0.15, seed=42, stratify_by_column="label") print(f"\nTrain: {len(splits['train'])}, Test: {len(splits['test'])}") for s in ['train', 'test']: for i, name in enumerate(LABELS): print(f" {s}/{name}: {splits[s]['label'].count(i)}") print("\nPushing to Hub...") splits.push_to_hub("AbijahKaj/telephony-amd-dataset", private=True) print("Done! https://huggingface.co/datasets/AbijahKaj/telephony-amd-dataset") if __name__ == "__main__": asyncio.run(main())