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88a679b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | import os
import torch
import torch.nn as nn
import json
import argparse
import soundfile as sf
import io
import numpy as np
import traceback
import re
import sys
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
from src.models.phoneme_embedder import Wav2Vec2PhonemeEmbedder
from src.g2p.g2p_utils import G2PManager
from src.utils.audio_utils import AudioPreprocessor
from collections import OrderedDict
def calculate_per(reference, hypothesis):
"""Memory-efficient Levenshtein distance for PER."""
nr = len(reference)
nh = len(hypothesis)
if nr == 0: return nh
if nh == 0: return nr
row = np.arange(nh + 1)
for i in range(1, nr + 1):
prev_row = row.copy()
row[0] = i
for j in range(1, nh + 1):
cost = 0 if reference[i-1] == hypothesis[j-1] else 1
row[j] = min(prev_row[j] + 1, # deletion
row[j-1] + 1, # insertion
prev_row[j-1] + cost) # substitution
return row[nh] / nr
def main():
# Ensure NLTK resources are available for G2P
import nltk
print("Checking NLTK resources...", flush=True)
for res in ['averaged_perceptron_tagger', 'averaged_perceptron_tagger_eng', 'cmudict', 'punkt', 'punkt_tab']:
try:
nltk.download(res, quiet=True)
except Exception:
pass
parser = argparse.ArgumentParser(description="Evaluate Phoneme Embedder on NPTEL dataset")
parser.add_argument("--model_dir", default="trained_models/20k_steps", help="Path to model directory")
parser.add_argument("--num_samples", type=int, default=100, help="Number of samples to evaluate")
parser.add_argument("--split", default="train", help="Dataset split")
parser.add_argument("--skip", type=int, default=50000, help="Skip first N samples")
parser.add_argument("--sanity_check", action="store_true", help="Run on training data (skip=0) to verify weights")
args = parser.parse_args()
if args.sanity_check:
print("🔍 SANITY CHECK MODE: Reverting skip to 0 to test training data.")
args.skip = 0
print(f"Loading model from {args.model_dir}...", flush=True)
processor = Wav2Vec2Processor.from_pretrained(args.model_dir)
model = Wav2Vec2PhonemeEmbedder.from_pretrained(args.model_dir)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
# Initialize G2P Manager and Audio Preprocessor (SYNC WITH TRAINING)
print("Initializing components (G2P and AudioPreprocessor)...", flush=True)
g2p_manager = G2PManager()
preprocessor_utils = AudioPreprocessor(sr=16000)
# Load ID to Phoneme mapping
vocab_path = os.path.join(args.model_dir, "vocab.json")
with open(vocab_path, "r", encoding="utf8") as f:
vocab = json.load(f)
id2phoneme = {v: k for k, v in vocab.items()}
pad_id = processor.tokenizer.pad_token_id
print(f"Loading dataset skbose/indian-english-nptel-v0 (streaming)...", flush=True)
ds = load_dataset("skbose/indian-english-nptel-v0", split=args.split, streaming=True)
ds = ds.cast_column("audio", Audio(decode=False))
print(f"Skipping {args.skip} samples...", flush=True)
eval_iterable = ds.skip(args.skip).take(args.num_samples)
total_per = 0
count = 0
iterator = iter(eval_iterable)
print(f"Evaluating {args.num_samples} samples...", flush=True)
for i in range(args.num_samples):
try:
# 0. Get Sample
try:
sample = next(iterator)
except (RuntimeError, Exception):
print(f"\n⚠️ HF Error at step {i}, re-initializing iterator...", flush=True)
ds_reinit = load_dataset("skbose/indian-english-nptel-v0", split=args.split, streaming=True)
ds_reinit = ds_reinit.cast_column("audio", Audio(decode=False))
iterator = iter(ds_reinit.skip(args.skip + i).take(args.num_samples - i))
sample = next(iterator)
# 1. Decode audio
audio_bytes = sample["audio"]["bytes"]
with io.BytesIO(audio_bytes) as f:
audio_array, sr = sf.read(f)
# Resample to 16kHz if needed (Matching train_streaming.py)
if sr != 16000:
import librosa
audio_array = librosa.resample(audio_array, orig_sr=sr, target_sr=16000)
# 1.1 SYNC PREPROCESSING: FFT Filter + VAD Trim
audio_data = preprocessor_utils.preprocess(audio_array)
# Skip extremely long audio
if len(audio_data) / 16000 > 30:
continue
# Ensure float32
audio_data = audio_data.astype(np.float32)
# 2. Preprocess with Processor (Group Norm happens here)
inputs = processor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(device)
# 3. Inference
with torch.no_grad():
outputs = model(input_values=input_values, return_dict=True)
if isinstance(outputs, (dict, OrderedDict)):
logits = outputs.get("logits")
elif hasattr(outputs, "logits"):
logits = outputs.logits
else:
logits = outputs[0] if isinstance(outputs, (tuple, list)) else outputs
pred_ids = torch.argmax(logits, dim=-1)[0].cpu().numpy()
# 4. Collapse CTC
collapsed = []
prev = None
unk_count = 0
for pid in pred_ids:
if pid != pad_id:
if pid == 1: unk_count += 1
if pid != prev:
collapsed.append(id2phoneme.get(int(pid), "<unk>"))
prev = pid
# 5. Transcription handling
trans = sample.get("transcription_normalised") or sample.get("transcription") or ""
trans = str(trans)
if not trans.strip(): continue
target_phonemes = g2p_manager.convert_sentence(trans)
# 6. PER
per = calculate_per(target_phonemes, collapsed)
total_per += per
count += 1
# Sample display
if i < 3 or (i % 20 == 0):
print(f"\n--- Sample {i+1} ---", flush=True)
print(f"Ref: {' '.join(target_phonemes[:20])}...", flush=True)
print(f"Hyp: {' '.join(collapsed[:20])}...", flush=True)
print(f"Stat: {len(pred_ids)} frames, {unk_count} <unk> frames.", flush=True)
print(f"PER: {per:.2%}", flush=True)
elif (i+1) % 5 == 0:
print(f"Processed {i+1}/{args.num_samples}...", end="\r", flush=True)
except StopIteration:
break
except Exception as e:
print(f"Error processing sample {i}: {e}", flush=True)
continue
if count > 0:
avg_per = total_per / count
print(f"\n\n{'='*40}")
print(f"FINAL RESULTS: PER = {avg_per:.2%}")
print(f"{'='*40}")
if avg_per > 0.8:
print("⚠️ WARNING: High PER detected. This usually indicates under-training or a vocab mismatch.")
print("👉 Try running with --sanity_check to see performance on training data.")
else:
print("\nNo samples were successfully evaluated.")
if __name__ == "__main__":
main()
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