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.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+Main/parakeet_final.nemo filter=lfs diff=lfs merge=lfs -text
+nemo_train_manifest.jsonl filter=lfs diff=lfs merge=lfs -text

.virtual_documents/__notebook_source__.ipynb

@@ -0,0 +1,427 @@
+# with internet 
+
+! apt-get install sox libsox-fmt-mp3 -y
+get_ipython().getoutput("apt-get install ffmpeg")
+get_ipython().getoutput("apt-get install libsndfile1")
+get_ipython().getoutput("pip install Cython")
+get_ipython().getoutput("pip install nemo_toolkit['all']")
+print('INSTALL OK !!!!!')
+
+
+import warnings
+
+# To ignore ALL warnings
+warnings.filterwarnings("ignore")
+
+
+import os
+import json
+import random
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import List
+
+import torch
+import nemo.collections.asr as nemo_asr
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks import ModelCheckpoint, Callback
+from lightning.pytorch.loggers import TensorBoardLogger
+from omegaconf import OmegaConf
+
+# =====================
+# Configuration Setup
+# =====================
+@dataclass
+class ASRConfig:
+    # --- Randomization ---
+    seed: int = 43
+
+    # --- File Paths ---
+    dataset_jsonl: str = "/kaggle/input/datasets/klaus011/data-csr/Data-CSR/train_word_transcripts.jsonl"
+    dataset_root: str = "/kaggle/input/datasets/klaus011/data-csr/Data-CSR/"
+    train_manifest: str = "/kaggle/working/nemo_train_manifest.jsonl"
+    val_manifest: str = "/kaggle/working/nemo_val_manifest.jsonl"
+    checkpoint_dir: str = "/kaggle/working/checkpoints"
+    log_dir: str = "/kaggle/working"
+    final_model_path: str = "/kaggle/working/Main/parakeet_final.nemo"
+
+    # --- Data Splitting ---
+    target_val_ratio: float = 0.10
+
+    # --- Model Configs ---
+    pretrained_model_name: str = "nvidia/parakeet-tdt-0.6b-v2"
+    num_layers_to_unfreeze: int = 10
+
+    # --- Dataloader Configs ---
+    sample_rate: int = 16000
+    batch_size: int = 32
+    num_workers: int = 16
+    min_duration: float = 0.5
+    max_duration: float = 40.0
+
+    # --- Augmentation Configs ---
+    aug_speed_prob: float = 0.5
+    aug_speed_min: float = 0.85
+    aug_speed_max: float = 1.15
+    
+    aug_shift_prob: float = 0.5
+    aug_shift_min_ms: float = -5.0
+    aug_shift_max_ms: float = 5.0
+
+    # Dropping
+    # noise_manifest: str = "/kaggle/working/nemo_noise_manifest.jsonl"
+    # aug_noise_prob: float = 0.5
+    # aug_noise_min_snr_db: float = 10.0   # Lower means louder noise
+    # aug_noise_max_snr_db: float = 30.0  # Higher means quieter noise
+
+
+    # aug_pitch_prob: float = 0.5
+    # aug_pitch_min_steps: int = -1  # Down 1 semitone
+    # aug_pitch_max_steps: int = 3   # Up 3 semitones (mimics younger voices)
+
+    # --- Optimizer Configs ---
+    learning_rate: float = 1e-5
+    min_lr: float = 1e-6
+    weight_decay: float = 1e-3
+    warmup_ratio: float = 0.05
+    betas: List[float] = field(default_factory=lambda: [0.9, 0.98])
+
+    # --- Trainer Configs ---
+    max_epochs: int = 5
+    precision: str = "bf16-mixed"
+    gradient_clip_val: float = 1.0
+    log_every_n_steps: int = 25
+    accumulate_grad_batches = 2
+
+
+# Instantiate the configuration
+cfg = ASRConfig()
+
+# =====================
+# Environment & Seeds
+# =====================
+random.seed(cfg.seed)
+
+os.environ["NEMO_ENABLE_CUDA_GRAPHS"] = "0"
+os.environ["CUDA_GRAPHS_DISABLE"] = "1"
+os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
+torch.set_float32_matmul_precision("high")
+
+
+# =====================
+# Custom Logger Callback
+# =====================
+class PrintLossCallback(Callback):
+    """Custom callback to print train/val metrics cleanly at the end of each epoch."""
+    
+    def on_train_epoch_end(self, trainer, pl_module):
+        metrics = trainer.callback_metrics
+        epoch = trainer.current_epoch
+        
+        # Retrieve metrics
+        val_loss = metrics.get("val_loss")
+        val_wer = metrics.get("val_wer")
+        train_loss = metrics.get("train_loss")
+        train_wer = metrics.get("training_batch_wer") 
+        
+        # Format strings safely
+        v_loss_str = f"{val_loss.item():.4f}" if val_loss is not None else "N/A"
+        t_loss_str = f"{train_loss.item():.4f}" if train_loss is not None else "N/A"
+        v_wer_str = f"{val_wer.item():.4f}" if val_wer is not None else "N/A"
+        t_wer_str = f"{train_wer.item():.4f}" if train_wer is not None else "N/A" 
+        
+        print(f"\n--- [Epoch {epoch}] Metrics ---")
+        print(f"Train Loss : {t_loss_str}")
+        print(f"Val Loss   : {v_loss_str}")
+        print(f"Train WER  : {t_wer_str}") 
+        print(f"Val WER    : {v_wer_str}")
+        print("--------------------------\n")
+
+
+# =====================
+# 1. Load Data & Split
+# =====================
+child_entries = defaultdict(list)
+child_age = {}
+child_utterance_count = defaultdict(int)
+
+with open(cfg.dataset_jsonl) as f:
+    for line in f:
+        data = json.loads(line)
+        cid = data["child_id"]
+        age = data["age_bucket"]
+        
+        # Assign the child to the age bucket where they have their FIRST utterance
+        if cid not in child_age:
+            child_age[cid] = age
+            
+        child_entries[cid].append(data)
+        child_utterance_count[cid] += 1
+
+# Group by Age
+age_children = defaultdict(list)
+age_total_utterances = defaultdict(int)
+
+for cid, age in child_age.items():
+    age_children[age].append(cid)
+    age_total_utterances[age] += child_utterance_count[cid]
+
+# Duration-Aware Stratified Child Split
+val_children = set()
+val_entries = []
+train_entries = []
+
+for age, children in age_children.items():
+    target_val_utterances = int(age_total_utterances[age] * cfg.target_val_ratio)
+    
+    random.shuffle(children)
+    
+    current_val_utterances = 0
+    age_val_children = []
+    
+    for cid in children:
+        if current_val_utterances + child_utterance_count[cid] > target_val_utterances and len(age_val_children) > 0:
+            continue
+            
+        age_val_children.append(cid)
+        val_children.add(cid)
+        current_val_utterances += child_utterance_count[cid]
+        
+        if current_val_utterances >= target_val_utterances:
+            break
+            
+    if len(age_val_children) == 0 and len(children) > 0:
+        val_children.add(children[0])
+
+# Build Train/Val lists
+for cid, entries in child_entries.items():
+    if cid in val_children:
+        val_entries.extend(entries)
+    else:
+        train_entries.extend(entries)
+
+# train_entries = train_entries[:150]
+# val_entries = val_entries[:40]
+
+print(f"Total children: {len(child_entries)}")
+print(f"Validation children: {len(val_children)} ({len(val_children)/len(child_entries):.1%})")
+print(f"Train samples: {len(train_entries)}")
+print(f"Val samples: {len(val_entries)} ({len(val_entries)/(len(train_entries)+len(val_entries)):.1%})")
+
+# =====================
+# 2. Export Manifests
+# =====================
+def export_to_nemo_manifest(entries, output_filename, dataset_root=""):
+    """Converts a list of dictionary entries into a NeMo-compatible JSONL manifest."""
+    # Ensure directory exists
+    os.makedirs(os.path.dirname(output_filename), exist_ok=True)
+    with open(output_filename, 'w', encoding='utf-8') as f:
+        for entry in entries:
+            nemo_record = {
+                "audio_filepath": os.path.join(dataset_root, entry["audio_path"]),
+                "duration": entry["audio_duration_sec"],
+                "text": entry["orthographic_text"],
+                "child_id": entry["child_id"],
+                "age_bucket": entry["age_bucket"],
+                "utterance_id": entry["utterance_id"]
+            }
+            f.write(json.dumps(nemo_record) + '\n')
+
+export_to_nemo_manifest(train_entries, cfg.train_manifest, cfg.dataset_root)
+export_to_nemo_manifest(val_entries, cfg.val_manifest, cfg.dataset_root)
+
+print(f"Exported {len(train_entries)} samples to {cfg.train_manifest}")
+print(f"Exported {len(val_entries)} samples to {cfg.val_manifest}")
+
+
+# =====================
+# 3. Model Setup
+# =====================
+model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(cfg.pretrained_model_name) 
+# 1. Ensure the whole model is unfrozen first 
+for param in model.parameters(): 
+    param.requires_grad = True
+
+
+# 2. Freeze all parameters in the encoder
+    
+for param in model.encoder.parameters(): 
+    param.requires_grad = False 
+
+    # 3. Unfreeze the last N layers of the ConformerEncoder 
+if hasattr(model.encoder, 'layers'): 
+    for layer in model.encoder.layers[-cfg.num_layers_to_unfreeze:]: 
+        for param in layer.parameters(): 
+            param.requires_grad = True 
+    print(f"Successfully unfrozen the last {cfg.num_layers_to_unfreeze} layers of the encoder.") 
+else: 
+    print("Warning: Could not locate encoder layers. Check model architecture.")
+
+
+# =====================
+# 4. Data Loaders
+# =====================
+model.setup_training_data({
+    "manifest_filepath": cfg.train_manifest,
+    "sample_rate": cfg.sample_rate,
+    "batch_size": cfg.batch_size,
+    "shuffle": True,
+    "num_workers": cfg.num_workers,
+    "pin_memory": True,
+    "max_duration": cfg.max_duration,
+    "min_duration": cfg.min_duration,
+    "augmentor": {
+        "speed": {
+            "prob": cfg.aug_speed_prob,
+            "sr": cfg.sample_rate,
+            "resample_type": "kaiser_fast",
+            "min_speed_rate": cfg.aug_speed_min, 
+            "max_speed_rate": cfg.aug_speed_max  
+        },
+        "shift": {
+            "prob": cfg.aug_shift_prob,
+            "min_shift_ms": cfg.aug_shift_min_ms,  
+            "max_shift_ms": cfg.aug_shift_max_ms    
+        }
+    }
+})
+
+model.setup_validation_data({
+    "manifest_filepath": cfg.val_manifest,
+    "sample_rate": cfg.sample_rate,
+    "batch_size": cfg.batch_size,
+    "shuffle": False,
+    "num_workers": cfg.num_workers,
+    "pin_memory": True,
+    "max_duration": cfg.max_duration,
+    "min_duration": cfg.min_duration
+})
+
+# =====================
+# 5. Optimization & Decoding
+# =====================
+decoding_cfg = OmegaConf.create({
+    "strategy": "greedy"
+})
+
+steps_per_epoch = len(train_entries) // cfg.batch_size
+total_steps = steps_per_epoch * cfg.max_epochs
+warmup_steps = int(total_steps * cfg.warmup_ratio)
+
+optim_cfg = OmegaConf.create({
+    "name": "adamw",
+    "lr": cfg.learning_rate,
+    "betas": cfg.betas,
+    "weight_decay": cfg.weight_decay,
+    "sched": {
+        "name": "CosineAnnealing",
+        "warmup_steps": warmup_steps,
+        "min_lr": cfg.min_lr,
+    }
+})
+
+model.setup_optimization(optim_cfg)
+model.change_decoding_strategy(decoding_cfg)
+
+# Disable the default printing of reference and predicted text
+if hasattr(model, 'decoding') and hasattr(model.decoding, 'wer'):
+    model.decoding.wer.log_prediction = False
+if hasattr(model, 'wer'):
+    model.wer.log_prediction = False
+
+# =====================
+# 6. Callbacks & Trainer
+# =====================
+logger = TensorBoardLogger(
+    save_dir=cfg.log_dir,
+    name="parakeet_finetune_"
+)
+
+checkpoint_callback = ModelCheckpoint(
+    dirpath=cfg.checkpoint_dir,
+    filename="parakeet-{epoch:02d}-{val_wer:.4f}",
+    monitor="val_wer",
+    mode="min",
+    save_top_k=2,
+    save_last=False,
+    verbose=False
+)
+
+trainer = pl.Trainer(
+    accelerator="gpu",
+    devices=1,
+    max_epochs=cfg.max_epochs,
+    precision=cfg.precision,
+    callbacks=[checkpoint_callback, PrintLossCallback()],
+    logger=logger,
+    # accumulate_grad_batches = cfg.accumulate_grad_batches,
+    log_every_n_steps=cfg.log_every_n_steps,
+    val_check_interval=1.0,
+    gradient_clip_val=cfg.gradient_clip_val
+)
+
+# =====================
+# 7. Execute Training
+# =====================
+trainer.fit(model)
+
+# Save final model
+os.makedirs(os.path.dirname(cfg.final_model_path), exist_ok=True)
+model.save_to(cfg.final_model_path)
+
+
+"One Hour"
+
+
+"two Hour"
+
+
+"three Hour"
+
+
+"4 Hour"
+
+
+"5 Hour"
+
+
+1+1+1
+
+
+1+1+1
+
+
+1+1+1
+
+
+from huggingface_hub import login, HfApi
+from kaggle_secrets import UserSecretsClient
+
+# Load token
+user_secrets = UserSecretsClient()
+HF_TOKEN = user_secrets.get_secret("HF_TOKEN")
+
+# Login
+login(token=HF_TOKEN)
+
+# Initialize API
+api = HfApi()
+
+repo_id = "jatinmehra/LowAUG-CSR"
+
+# Create DATASET repo
+api.create_repo(
+    repo_id=repo_id,
+    repo_type="dataset",
+    exist_ok=True,
+    private=True
+)
+
+# Upload folder to DATASET repo
+api.upload_folder(
+    folder_path="/kaggle/working/",
+    repo_id=repo_id,
+    repo_type="dataset"
+)
+
+print(f"Success: https://huggingface.co/datasets/{repo_id}")

Main/parakeet_final.nemo

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+size 2472222720

checkpoints/parakeet-epoch=03-val_wer=0.1319.ckpt

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+size 4559221842

checkpoints/parakeet-epoch=04-val_wer=0.1327.ckpt

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+size 4559221842

nemo_train_manifest.jsonl

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+size 25198069

parakeet_finetune_/version_0/events.out.tfevents.1771744988.7786a8438749.553.0

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+size 161059

parakeet_finetune_/version_0/hparams.yaml

@@ -0,0 +1,2239 @@
+cfg:
+  sample_rate: 16000
+  compute_eval_loss: false
+  log_prediction: true
+  rnnt_reduction: mean_volume
+  skip_nan_grad: false
+  model_defaults:
+    enc_hidden: 1024
+    pred_hidden: 640
+    joint_hidden: 640
+    tdt_durations:
+    - 0
+    - 1
+    - 2
+    - 3
+    - 4
+    num_tdt_durations: 5
+  train_ds:
+    manifest_filepath: /kaggle/working/nemo_train_manifest.jsonl
+    sample_rate: 16000
+    batch_size: 32
+    shuffle: true
+    num_workers: 16
+    pin_memory: true
+    max_duration: 40.0
+    min_duration: 0.5
+    augmentor:
+      speed:
+        prob: 0.5
+        sr: 16000
+        resample_type: kaiser_fast
+        min_speed_rate: 0.85
+        max_speed_rate: 1.15
+      shift:
+        prob: 0.5
+        min_shift_ms: -5.0
+        max_shift_ms: 5.0
+  validation_ds:
+    manifest_filepath: /kaggle/working/nemo_val_manifest.jsonl
+    sample_rate: 16000
+    batch_size: 32
+    shuffle: false
+    num_workers: 16
+    pin_memory: true
+    max_duration: 40.0
+    min_duration: 0.5
+  tokenizer:
+    dir: /lustre/fsw/portfolios/llmservice/users/nkoluguri/datasets/tokenizers/en_mm_ytc/tokenizer_spe_bpe_v1024/
+    type: bpe
+    model_path: nemo:705f11d22dc04b169effc35ce5cd1361_tokenizer.model
+    vocab_path: nemo:4cf78c8ca4ca44fca36c3754478fb188_vocab.txt
+    spe_tokenizer_vocab: nemo:a4715c7f6b2d4c2bb709306073d0c0a4_tokenizer.vocab
+  preprocessor:
+    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
+    sample_rate: 16000
+    normalize: per_feature
+    window_size: 0.025
+    window_stride: 0.01
+    window: hann
+    features: 128
+    n_fft: 512
+    log: true
+    frame_splicing: 1
+    dither: 1.0e-05
+    pad_to: 0
+    pad_value: 0.0
+  spec_augment:
+    _target_: nemo.collections.asr.modules.SpectrogramAugmentation
+    freq_masks: 2
+    time_masks: 10
+    freq_width: 27
+    time_width: 0.05
+  encoder:
+    _target_: nemo.collections.asr.modules.ConformerEncoder
+    feat_in: 128
+    feat_out: -1
+    n_layers: 24
+    d_model: 1024
+    use_bias: false
+    subsampling: dw_striding
+    subsampling_factor: 8
+    subsampling_conv_channels: 256
+    causal_downsampling: false
+    reduction: null
+    reduction_position: null
+    reduction_factor: 1
+    ff_expansion_factor: 4
+    self_attention_model: rel_pos
+    n_heads: 8
+    att_context_size:
+    - -1
+    - -1
+    att_context_style: regular
+    xscaling: false
+    untie_biases: true
+    pos_emb_max_len: 5000
+    conv_kernel_size: 9
+    conv_norm_type: batch_norm
+    conv_context_size: null
+    dropout: 0.1
+    dropout_pre_encoder: 0.1
+    dropout_emb: 0.0
+    dropout_att: 0.1
+    stochastic_depth_drop_prob: 0.0
+    stochastic_depth_mode: linear
+    stochastic_depth_start_layer: 1
+  decoder:
+    _target_: nemo.collections.asr.modules.RNNTDecoder
+    normalization_mode: null
+    random_state_sampling: false
+    blank_as_pad: true
+    prednet:
+      pred_hidden: 640
+      pred_rnn_layers: 2
+      t_max: null
+      dropout: 0.2
+    vocab_size: 1024
+  joint:
+    _target_: nemo.collections.asr.modules.RNNTJoint
+    log_softmax: null
+    preserve_memory: false
+    fuse_loss_wer: true
+    fused_batch_size: 4
+    jointnet:
+      joint_hidden: 640
+      activation: relu
+      dropout: 0.2
+      encoder_hidden: 1024
+      pred_hidden: 640
+    num_extra_outputs: 5
+    num_classes: 1024
+    vocabulary:
+    - <unk>
+    - ▁t
+    - ▁th
+    - ▁a
+    - in
+    - ▁the
+    - re
+    - ▁w
+    - ▁o
+    - ▁s
+    - at
+    - ou
+    - er
+    - nd
+    - ▁i
+    - ▁b
+    - ▁c
+    - 'on'
+    - ▁h
+    - ing
+    - ▁to
+    - ▁m
+    - en
+    - ▁f
+    - ▁p
+    - an
+    - ▁d
+    - es
+    - or
+    - ll
+    - ▁of
+    - ▁and
+    - ▁y
+    - ▁l
+    - ▁I
+    - it
+    - ▁in
+    - is
+    - ed
+    - ▁g
+    - ▁you
+    - ar
+    - ▁that
+    - om
+    - as
+    - ▁n
+    - ve
+    - us
+    - ic
+    - ow
+    - al
+    - ▁it
+    - ▁be
+    - ▁wh
+    - le
+    - ion
+    - ut
+    - ot
+    - ▁we
+    - ▁is
+    - ▁e
+    - et
+    - ay
+    - ▁re
+    - ▁on
+    - ▁T
+    - ▁A
+    - ▁ha
+    - ent
+    - ke
+    - ct
+    - ▁S
+    - ig
+    - ver
+    - ▁Th
+    - all
+    - id
+    - ▁for
+    - ro
+    - ▁he
+    - se
+    - ▁this
+    - ld
+    - ly
+    - ▁go
+    - ▁k
+    - ▁st
+    - st
+    - ch
+    - ▁li
+    - ▁u
+    - am
+    - ur
+    - ce
+    - ith
+    - im
+    - ▁so
+    - ▁have
+    - ▁do
+    - ht
+    - th
+    - ▁an
+    - ▁with
+    - ad
+    - ▁r
+    - ir
+    - ▁was
+    - ▁as
+    - ▁W
+    - ▁are
+    - ust
+    - ally
+    - ▁j
+    - ▁se
+    - ation
+    - od
+    - ere
+    - ▁like
+    - ▁not
+    - ▁kn
+    - ight
+    - ▁B
+    - ▁they
+    - ▁And
+    - ▁know
+    - ome
+    - op
+    - ▁can
+    - ▁or
+    - ▁sh
+    - ▁me
+    - ill
+    - ant
+    - ck
+    - ▁what
+    - ▁at
+    - ▁ab
+    - ould
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+    - $
+    - Z
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+    - é
+    - /
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+    - á
+    - £
+    - ó
+    - ā
+    - ü
+    - ñ
+    - ö
+    - è
+    - ç
+    - à
+    - ¿
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+    - π
+    - ä
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+    - ë
+    - н
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+    - š
+    - Ü
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+    - я
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+    - Ö
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+    - ο
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+    - к
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+    model_type: tdt
+    durations:
+    - 0
+    - 1
+    - 2
+    - 3
+    - 4
+    greedy:
+      max_symbols: 10
+    beam:
+      beam_size: 2
+      return_best_hypothesis: false
+      score_norm: true
+      tsd_max_sym_exp: 50
+      alsd_max_target_len: 2.0
+  aux_ctc:
+    ctc_loss_weight: 0.3
+    use_cer: false
+    ctc_reduction: mean_batch
+    decoder:
+      _target_: nemo.collections.asr.modules.ConvASRDecoder
+      feat_in: null
+      num_classes: -1
+      vocabulary: []
+    decoding:
+      strategy: greedy
+  interctc:
+    loss_weights: []
+    apply_at_layers: []
+  loss:
+    loss_name: tdt
+    tdt_kwargs:
+      fastemit_lambda: 0.0
+      clamp: -1.0
+      durations:
+      - 0
+      - 1
+      - 2
+      - 3
+      - 4
+      sigma: 0.02
+      omega: 0.1
+  optim:
+    name: adamw
+    lr: 0.0001
+    betas:
+    - 0.9
+    - 0.98
+    weight_decay: 0.001
+    sched:
+      name: CosineAnnealing
+      warmup_steps: 0
+      warmup_ratio: null
+      min_lr: 1.0e-06
+  labels:
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+  - ▁th
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+  - K
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+  - V
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+  - '6'
+  - '7'
+  - '!'
+  - '%'
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+  - $
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+  - X
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+  - /
+  - í
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+  - £
+  - ó
+  - ā
+  - ü
+  - ñ
+  - ö
+  - è
+  - ç
+  - à
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+  - π
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+  - а
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+  - γ
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+  - χ
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+  - ě
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+  - П
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+  - Î
+  - Ā
+  - ė
+  - Š
+  - ź
+  - Κ
+  - Ψ
+  - ά
+  - ξ
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+  target: nemo.collections.asr.models.rnnt_bpe_models.EncDecRNNTBPEModel
+  nemo_version: 2.4.0rc0