diff --git a/app.py b/app.py
index 4c8e93495f61e9d51897940fcfafb88fa616e307..0d9ace1655b0c32b3dae6f39d4c048f8bdea9497 100644
--- a/app.py
+++ b/app.py
@@ -3,70 +3,63 @@ from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 from logic import synthesize_voice, plot_data, plot_waveforms
 import base64
-import sys
-import numpy as np
-from io import BytesIO
-from hifigan.inference_e2e import hifi_gan_inference
+from typing import Dict
 
 app = FastAPI()
 
-@app.get("/")
-def read_root():
-    data = {"Voice": "Cloning", "Status": "Success"}
-    return JSONResponse(content=data)
 
+# You need to replace the placeholders above with the actual URLs for the models.
+
+# Allow requests from your Vercel domain
+origins = [
+    "https://host-test-smoky.vercel.app",
+    # Add other allowed origins if needed
+]
+
+# Set up CORS middleware
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
+    allow_origins=origins,
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
-hugging_face_api_url = "https://huggingface.co/spaces/lord-reso/host/synthesize"
+@app.post("/synthesize", response_model=Dict[str, str])
+async def synthesize(request_data: Dict[str, str]):
+    font_type = request_data['font_select']
+    input_text = request_data['input_text']
 
-@app.post("/synthesize")
-async def synthesize(request: Request):
-    print("call successful")
-    
-    json = await request.json()
-    print(json)
-    
-    font_type = json['font_select']
-    input_text = json['input_text']     
+    # Font selection logic (customize based on your requirements)
+    if font_type == 'Preeti':
+        # Implement Preeti font logic
+        pass
+    elif font_type == 'Unicode':
+        # Implement Unicode font logic
+        pass
 
-    print("generating mel-spectrogram")
     # Generate mel-spectrogram using Tacotron2
-    # mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned.pt")
-    mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "kaggle_12000.pt")
-    print("mel generation successful")
-        
+    mel_output_data, mel_output_postnet_data, alignments_data = synthesize_voice(input_text, "Shruti_finetuned")
+
     # Convert mel-spectrogram to base64 for display in HTML
     mel_output_base64 = plot_data([mel_output_data, mel_output_postnet_data, alignments_data])
 
-    # Audio Synthesis begins
-    print("Starting audio synthesis")
-    buffer = BytesIO()
-    np.save(buffer, mel_output_data)
-    input_mel = buffer.getvalue()
-    
-    hifigan_checkpoint = "generator_v1"
-    
-    # Generate audio using Hifigan
-    audio_data = hifi_gan_inference(input_mel, hifigan_checkpoint)
+    # Save the generated audio file
+    audio_file_path = 'audio_output/mel1_generated_e2e.wav'
 
-    print("Creating time-domain waveform")
     # Plot the waveform
-    wave_base64 = plot_waveforms(audio_data)
+    wave_base64 = plot_waveforms(audio_file_path)
 
     # Encode audio content as Base64
-    audio_base64 = base64.b64encode(audio_data).decode('utf-8')
+    with open(audio_file_path, 'rb') as audio_file:
+        audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')
 
     # Customize the response based on the information you want to send to the frontend
     response_data = {
         'mel_spectrogram': mel_output_base64,
         'audio_data': audio_base64,
         'waveform': wave_base64,
+        'some_other_data': 'example_value',
     }
 
-    return JSONResponse(content=response_data)
\ No newline at end of file
+    return JSONResponse(content=response_data)
diff --git a/distributed.py b/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..cce74944bfec6a69ac50921ec82a74d4874d6070
--- /dev/null
+++ b/distributed.py
@@ -0,0 +1,173 @@
+import torch
+import torch.distributed as dist
+from torch.nn.modules import Module
+from torch.autograd import Variable
+
+def _flatten_dense_tensors(tensors):
+    """Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
+    same dense type.
+    Since inputs are dense, the resulting tensor will be a concatenated 1D
+    buffer. Element-wise operation on this buffer will be equivalent to
+    operating individually.
+    Arguments:
+        tensors (Iterable[Tensor]): dense tensors to flatten.
+    Returns:
+        A contiguous 1D buffer containing input tensors.
+    """
+    if len(tensors) == 1:
+        return tensors[0].contiguous().view(-1)
+    flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
+    return flat
+
+def _unflatten_dense_tensors(flat, tensors):
+    """View a flat buffer using the sizes of tensors. Assume that tensors are of
+    same dense type, and that flat is given by _flatten_dense_tensors.
+    Arguments:
+        flat (Tensor): flattened dense tensors to unflatten.
+        tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
+          unflatten flat.
+    Returns:
+        Unflattened dense tensors with sizes same as tensors and values from
+        flat.
+    """
+    outputs = []
+    offset = 0
+    for tensor in tensors:
+        numel = tensor.numel()
+        outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
+        offset += numel
+    return tuple(outputs)
+
+
+'''
+This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
+launcher included with this example. It assumes that your run is using multiprocess with 1
+GPU/process, that the model is on the correct device, and that torch.set_device has been
+used to set the device.
+
+Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
+and will be allreduced at the finish of the backward pass.
+'''
+class DistributedDataParallel(Module):
+
+    def __init__(self, module):
+        super(DistributedDataParallel, self).__init__()
+        #fallback for PyTorch 0.3
+        if not hasattr(dist, '_backend'):
+            self.warn_on_half = True
+        else:
+            self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
+
+        self.module = module
+
+        for p in self.module.state_dict().values():
+            if not torch.is_tensor(p):
+                continue
+            dist.broadcast(p, 0)
+
+        def allreduce_params():
+            if(self.needs_reduction):
+                self.needs_reduction = False
+                buckets = {}
+                for param in self.module.parameters():
+                    if param.requires_grad and param.grad is not None:
+                        tp = type(param.data)
+                        if tp not in buckets:
+                            buckets[tp] = []
+                        buckets[tp].append(param)
+                if self.warn_on_half:
+                    if torch.cuda.HalfTensor in buckets:
+                        print("WARNING: gloo dist backend for half parameters may be extremely slow." +
+                              " It is recommended to use the NCCL backend in this case. This currently requires" +
+                              "PyTorch built from top of tree master.")
+                        self.warn_on_half = False
+
+                for tp in buckets:
+                    bucket = buckets[tp]
+                    grads = [param.grad.data for param in bucket]
+                    coalesced = _flatten_dense_tensors(grads)
+                    dist.all_reduce(coalesced)
+                    coalesced /= dist.get_world_size()
+                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
+                        buf.copy_(synced)
+
+        for param in list(self.module.parameters()):
+            def allreduce_hook(*unused):
+                param._execution_engine.queue_callback(allreduce_params)
+            if param.requires_grad:
+                param.register_hook(allreduce_hook)
+
+    def forward(self, *inputs, **kwargs):
+        self.needs_reduction = True
+        return self.module(*inputs, **kwargs)
+
+    '''
+    def _sync_buffers(self):
+        buffers = list(self.module._all_buffers())
+        if len(buffers) > 0:
+            # cross-node buffer sync
+            flat_buffers = _flatten_dense_tensors(buffers)
+            dist.broadcast(flat_buffers, 0)
+            for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
+                buf.copy_(synced)
+     def train(self, mode=True):
+        # Clear NCCL communicator and CUDA event cache of the default group ID,
+        # These cache will be recreated at the later call. This is currently a
+        # work-around for a potential NCCL deadlock.
+        if dist._backend == dist.dist_backend.NCCL:
+            dist._clear_group_cache()
+        super(DistributedDataParallel, self).train(mode)
+        self.module.train(mode)
+    '''
+'''
+Modifies existing model to do gradient allreduce, but doesn't change class
+so you don't need "module"
+'''
+def apply_gradient_allreduce(module):
+        if not hasattr(dist, '_backend'):
+            module.warn_on_half = True
+        else:
+            module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
+
+        for p in module.state_dict().values():
+            if not torch.is_tensor(p):
+                continue
+            dist.broadcast(p, 0)
+
+        def allreduce_params():
+            if(module.needs_reduction):
+                module.needs_reduction = False
+                buckets = {}
+                for param in module.parameters():
+                    if param.requires_grad and param.grad is not None:
+                        tp = param.data.dtype
+                        if tp not in buckets:
+                            buckets[tp] = []
+                        buckets[tp].append(param)
+                if module.warn_on_half:
+                    if torch.cuda.HalfTensor in buckets:
+                        print("WARNING: gloo dist backend for half parameters may be extremely slow." +
+                              " It is recommended to use the NCCL backend in this case. This currently requires" +
+                              "PyTorch built from top of tree master.")
+                        module.warn_on_half = False
+
+                for tp in buckets:
+                    bucket = buckets[tp]
+                    grads = [param.grad.data for param in bucket]
+                    coalesced = _flatten_dense_tensors(grads)
+                    dist.all_reduce(coalesced)
+                    coalesced /= dist.get_world_size()
+                    for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
+                        buf.copy_(synced)
+
+        for param in list(module.parameters()):
+            def allreduce_hook(*unused):
+                Variable._execution_engine.queue_callback(allreduce_params)
+            if param.requires_grad:
+                param.register_hook(allreduce_hook)
+
+        def set_needs_reduction(self, input, output):
+            self.needs_reduction = True
+
+        module.register_forward_hook(set_needs_reduction)
+        return module
diff --git a/encoder/audio.py b/encoder/audio.py
deleted file mode 100644
index 799aa835499ce8b839290f28b2c8ffb629f37565..0000000000000000000000000000000000000000
--- a/encoder/audio.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from scipy.ndimage.morphology import binary_dilation
-from encoder.params_data import *
-from pathlib import Path
-from typing import Optional, Union
-from warnings import warn
-import numpy as np
-import librosa
-import struct
-
-try:
-    import webrtcvad
-except:
-    warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.")
-    webrtcvad=None
-
-int16_max = (2 ** 15) - 1
-
-
-def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
-                   source_sr: Optional[int] = None,
-                   normalize: Optional[bool] = True,
-                   trim_silence: Optional[bool] = True):
-    """
-    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
-    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
-    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
-    just .wav), either the waveform as a numpy array of floats.
-    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
-    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
-    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
-    this argument will be ignored.
-    """
-    # Load the wav from disk if needed
-    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
-        wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)
-    else:
-        wav = fpath_or_wav
-    
-    # Resample the wav if needed
-    if source_sr is not None and source_sr != sampling_rate:
-        wav = librosa.resample(wav, source_sr, sampling_rate)
-        
-    # Apply the preprocessing: normalize volume and shorten long silences 
-    if normalize:
-        wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
-    if webrtcvad and trim_silence:
-        wav = trim_long_silences(wav)
-    
-    return wav
-
-
-def wav_to_mel_spectrogram(wav):
-    """
-    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
-    Note: this not a log-mel spectrogram.
-    """
-    frames = librosa.feature.melspectrogram(
-        wav,
-        sampling_rate,
-        n_fft=int(sampling_rate * mel_window_length / 1000),
-        hop_length=int(sampling_rate * mel_window_step / 1000),
-        n_mels=mel_n_channels
-    )
-    return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
-    """
-    Ensures that segments without voice in the waveform remain no longer than a 
-    threshold determined by the VAD parameters in params.py.
-
-    :param wav: the raw waveform as a numpy array of floats 
-    :return: the same waveform with silences trimmed away (length <= original wav length)
-    """
-    # Compute the voice detection window size
-    samples_per_window = (vad_window_length * sampling_rate) // 1000
-    
-    # Trim the end of the audio to have a multiple of the window size
-    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
-    
-    # Convert the float waveform to 16-bit mono PCM
-    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
-    
-    # Perform voice activation detection
-    voice_flags = []
-    vad = webrtcvad.Vad(mode=3)
-    for window_start in range(0, len(wav), samples_per_window):
-        window_end = window_start + samples_per_window
-        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
-                                         sample_rate=sampling_rate))
-    voice_flags = np.array(voice_flags)
-    
-    # Smooth the voice detection with a moving average
-    def moving_average(array, width):
-        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
-        ret = np.cumsum(array_padded, dtype=float)
-        ret[width:] = ret[width:] - ret[:-width]
-        return ret[width - 1:] / width
-    
-    audio_mask = moving_average(voice_flags, vad_moving_average_width)
-    audio_mask = np.round(audio_mask).astype(np.bool)
-    
-    # Dilate the voiced regions
-    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
-    audio_mask = np.repeat(audio_mask, samples_per_window)
-    
-    return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
-    if increase_only and decrease_only:
-        raise ValueError("Both increase only and decrease only are set")
-    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
-    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
-        return wav
-    return wav * (10 ** (dBFS_change / 20))
diff --git a/encoder/config.py b/encoder/config.py
deleted file mode 100644
index 1c21312f3de971bfa008254c6035cebc09f05e4c..0000000000000000000000000000000000000000
--- a/encoder/config.py
+++ /dev/null
@@ -1,45 +0,0 @@
-librispeech_datasets = {
-    "train": {
-        "clean": ["LibriSpeech/train-clean-100", "LibriSpeech/train-clean-360"],
-        "other": ["LibriSpeech/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriSpeech/test-clean"],
-        "other": ["LibriSpeech/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriSpeech/dev-clean"],
-        "other": ["LibriSpeech/dev-other"]
-    },
-}
-libritts_datasets = {
-    "train": {
-        "clean": ["LibriTTS/train-clean-100", "LibriTTS/train-clean-360"],
-        "other": ["LibriTTS/train-other-500"]
-    },
-    "test": {
-        "clean": ["LibriTTS/test-clean"],
-        "other": ["LibriTTS/test-other"]
-    },
-    "dev": {
-        "clean": ["LibriTTS/dev-clean"],
-        "other": ["LibriTTS/dev-other"]
-    },
-}
-voxceleb_datasets = {
-    "voxceleb1" : {
-        "train": ["VoxCeleb1/wav"],
-        "test": ["VoxCeleb1/test_wav"]
-    },
-    "voxceleb2" : {
-        "train": ["VoxCeleb2/dev/aac"],
-        "test": ["VoxCeleb2/test_wav"]
-    }
-}
-
-other_datasets = [
-    "LJSpeech-1.1",
-    "VCTK-Corpus/wav48",
-]
-
-anglophone_nationalites = ["australia", "canada", "ireland", "uk", "usa"]
diff --git a/encoder/data_objects/__init__.py b/encoder/data_objects/__init__.py
deleted file mode 100644
index ef04ade68544d0477a7f6deb4e7d51e97f592910..0000000000000000000000000000000000000000
--- a/encoder/data_objects/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader
diff --git a/encoder/data_objects/random_cycler.py b/encoder/data_objects/random_cycler.py
deleted file mode 100644
index c405db6b27f46d874d8feb37e3f9c1e12c251109..0000000000000000000000000000000000000000
--- a/encoder/data_objects/random_cycler.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import random
-
-class RandomCycler:
-    """
-    Creates an internal copy of a sequence and allows access to its items in a constrained random 
-    order. For a source sequence of n items and one or several consecutive queries of a total 
-    of m items, the following guarantees hold (one implies the other):
-        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
-        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
-    """
-    
-    def __init__(self, source):
-        if len(source) == 0:
-            raise Exception("Can't create RandomCycler from an empty collection")
-        self.all_items = list(source)
-        self.next_items = []
-    
-    def sample(self, count: int):
-        shuffle = lambda l: random.sample(l, len(l))
-        
-        out = []
-        while count > 0:
-            if count >= len(self.all_items):
-                out.extend(shuffle(list(self.all_items)))
-                count -= len(self.all_items)
-                continue
-            n = min(count, len(self.next_items))
-            out.extend(self.next_items[:n])
-            count -= n
-            self.next_items = self.next_items[n:]
-            if len(self.next_items) == 0:
-                self.next_items = shuffle(list(self.all_items))
-        return out
-    
-    def __next__(self):
-        return self.sample(1)[0]
-
diff --git a/encoder/data_objects/speaker.py b/encoder/data_objects/speaker.py
deleted file mode 100644
index 494e882fe34fc38dcc793ab8c74a6cc2376bb7b5..0000000000000000000000000000000000000000
--- a/encoder/data_objects/speaker.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from encoder.data_objects.random_cycler import RandomCycler
-from encoder.data_objects.utterance import Utterance
-from pathlib import Path
-
-# Contains the set of utterances of a single speaker
-class Speaker:
-    def __init__(self, root: Path):
-        self.root = root
-        self.name = root.name
-        self.utterances = None
-        self.utterance_cycler = None
-        
-    def _load_utterances(self):
-        with self.root.joinpath("_sources.txt").open("r") as sources_file:
-            sources = [l.split(",") for l in sources_file]
-        sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
-        self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
-        self.utterance_cycler = RandomCycler(self.utterances)
-               
-    def random_partial(self, count, n_frames):
-        """
-        Samples a batch of <count> unique partial utterances from the disk in a way that all 
-        utterances come up at least once every two cycles and in a random order every time.
-        
-        :param count: The number of partial utterances to sample from the set of utterances from 
-        that speaker. Utterances are guaranteed not to be repeated if <count> is not larger than 
-        the number of utterances available.
-        :param n_frames: The number of frames in the partial utterance.
-        :return: A list of tuples (utterance, frames, range) where utterance is an Utterance, 
-        frames are the frames of the partial utterances and range is the range of the partial 
-        utterance with regard to the complete utterance.
-        """
-        if self.utterances is None:
-            self._load_utterances()
-
-        utterances = self.utterance_cycler.sample(count)
-
-        a = [(u,) + u.random_partial(n_frames) for u in utterances]
-
-        return a
diff --git a/encoder/data_objects/speaker_batch.py b/encoder/data_objects/speaker_batch.py
deleted file mode 100644
index e219b738e58a4450c8ac43e436ff57900832320c..0000000000000000000000000000000000000000
--- a/encoder/data_objects/speaker_batch.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-from typing import List
-from encoder.data_objects.speaker import Speaker
-
-
-class SpeakerBatch:
-    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
-        self.speakers = speakers
-        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
-
-        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
-        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
-        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])
diff --git a/encoder/data_objects/speaker_verification_dataset.py b/encoder/data_objects/speaker_verification_dataset.py
deleted file mode 100644
index 77a6e05eae6a939ae7575ae70b7173644141fffe..0000000000000000000000000000000000000000
--- a/encoder/data_objects/speaker_verification_dataset.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from encoder.data_objects.random_cycler import RandomCycler
-from encoder.data_objects.speaker_batch import SpeakerBatch
-from encoder.data_objects.speaker import Speaker
-from encoder.params_data import partials_n_frames
-from torch.utils.data import Dataset, DataLoader
-from pathlib import Path
-
-# TODO: improve with a pool of speakers for data efficiency
-
-class SpeakerVerificationDataset(Dataset):
-    def __init__(self, datasets_root: Path):
-        self.root = datasets_root
-        speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
-        if len(speaker_dirs) == 0:
-            raise Exception("No speakers found. Make sure you are pointing to the directory "
-                            "containing all preprocessed speaker directories.")
-        self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
-        self.speaker_cycler = RandomCycler(self.speakers)
-
-    def __len__(self):
-        return int(1e10)
-        
-    def __getitem__(self, index):
-        return next(self.speaker_cycler)
-    
-    def get_logs(self):
-        log_string = ""
-        for log_fpath in self.root.glob("*.txt"):
-            with log_fpath.open("r") as log_file:
-                log_string += "".join(log_file.readlines())
-        return log_string
-    
-    
-class SpeakerVerificationDataLoader(DataLoader):
-    def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None, 
-                 batch_sampler=None, num_workers=0, pin_memory=False, timeout=0, 
-                 worker_init_fn=None):
-        self.utterances_per_speaker = utterances_per_speaker
-
-        super().__init__(
-            dataset=dataset, 
-            batch_size=speakers_per_batch, 
-            shuffle=False, 
-            sampler=sampler, 
-            batch_sampler=batch_sampler, 
-            num_workers=num_workers,
-            collate_fn=self.collate, 
-            pin_memory=pin_memory, 
-            drop_last=False, 
-            timeout=timeout, 
-            worker_init_fn=worker_init_fn
-        )
-
-    def collate(self, speakers):
-        return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames) 
-    
\ No newline at end of file
diff --git a/encoder/data_objects/utterance.py b/encoder/data_objects/utterance.py
deleted file mode 100644
index 0768c3420f422a7464f305b4c1fb6752c57ceda7..0000000000000000000000000000000000000000
--- a/encoder/data_objects/utterance.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-
-class Utterance:
-    def __init__(self, frames_fpath, wave_fpath):
-        self.frames_fpath = frames_fpath
-        self.wave_fpath = wave_fpath
-        
-    def get_frames(self):
-        return np.load(self.frames_fpath)
-
-    def random_partial(self, n_frames):
-        """
-        Crops the frames into a partial utterance of n_frames
-        
-        :param n_frames: The number of frames of the partial utterance
-        :return: the partial utterance frames and a tuple indicating the start and end of the 
-        partial utterance in the complete utterance.
-        """
-        frames = self.get_frames()
-        if frames.shape[0] == n_frames:
-            start = 0
-        else:
-            start = np.random.randint(0, frames.shape[0] - n_frames)
-        end = start + n_frames
-        return frames[start:end], (start, end)
\ No newline at end of file
diff --git a/encoder/inference.py b/encoder/inference.py
deleted file mode 100644
index 43862e43e663dc5b2053c0f784dfac98cb0bacb3..0000000000000000000000000000000000000000
--- a/encoder/inference.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from encoder.params_data import *
-from encoder.model import SpeakerEncoder
-from encoder.audio import preprocess_wav   # We want to expose this function from here
-from matplotlib import cm
-from encoder import audio
-from pathlib import Path
-import numpy as np
-import torch
-
-_model = None # type: SpeakerEncoder
-_device = None # type: torch.device
-
-
-def load_model(weights_fpath: Path, device=None):
-    """
-    Loads the model in memory. If this function is not explicitely called, it will be run on the
-    first call to embed_frames() with the default weights file.
-
-    :param weights_fpath: the path to saved model weights.
-    :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The
-    model will be loaded and will run on this device. Outputs will however always be on the cpu.
-    If None, will default to your GPU if it"s available, otherwise your CPU.
-    """
-    # TODO: I think the slow loading of the encoder might have something to do with the device it
-    #   was saved on. Worth investigating.
-    global _model, _device
-    if device is None:
-        _device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    elif isinstance(device, str):
-        _device = torch.device(device)
-    _model = SpeakerEncoder(_device, torch.device("cpu"))
-    checkpoint = torch.load(weights_fpath, _device)
-    _model.load_state_dict(checkpoint["model_state"])
-    _model.eval()
-    print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"]))
-
-
-def is_loaded():
-    return _model is not None
-
-
-def embed_frames_batch(frames_batch):
-    """
-    Computes embeddings for a batch of mel spectrogram.
-
-    :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape
-    (batch_size, n_frames, n_channels)
-    :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
-    """
-    if _model is None:
-        raise Exception("Model was not loaded. Call load_model() before inference.")
-
-    frames = torch.from_numpy(frames_batch).to(_device)
-    embed = _model.forward(frames).detach().cpu().numpy()
-    return embed
-
-
-def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames,
-                           min_pad_coverage=0.75, overlap=0.5):
-    """
-    Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain
-    partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel
-    spectrogram slices are returned, so as to make each partial utterance waveform correspond to
-    its spectrogram. This function assumes that the mel spectrogram parameters used are those
-    defined in params_data.py.
-
-    The returned ranges may be indexing further than the length of the waveform. It is
-    recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
-
-    :param n_samples: the number of samples in the waveform
-    :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial
-    utterance
-    :param min_pad_coverage: when reaching the last partial utterance, it may or may not have
-    enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present,
-    then the last partial utterance will be considered, as if we padded the audio. Otherwise,
-    it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial
-    utterance, this parameter is ignored so that the function always returns at least 1 slice.
-    :param overlap: by how much the partial utterance should overlap. If set to 0, the partial
-    utterances are entirely disjoint.
-    :return: the waveform slices and mel spectrogram slices as lists of array slices. Index
-    respectively the waveform and the mel spectrogram with these slices to obtain the partial
-    utterances.
-    """
-    assert 0 <= overlap < 1
-    assert 0 < min_pad_coverage <= 1
-
-    samples_per_frame = int((sampling_rate * mel_window_step / 1000))
-    n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-    frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1)
-
-    # Compute the slices
-    wav_slices, mel_slices = [], []
-    steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
-    for i in range(0, steps, frame_step):
-        mel_range = np.array([i, i + partial_utterance_n_frames])
-        wav_range = mel_range * samples_per_frame
-        mel_slices.append(slice(*mel_range))
-        wav_slices.append(slice(*wav_range))
-
-    # Evaluate whether extra padding is warranted or not
-    last_wav_range = wav_slices[-1]
-    coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
-    if coverage < min_pad_coverage and len(mel_slices) > 1:
-        mel_slices = mel_slices[:-1]
-        wav_slices = wav_slices[:-1]
-
-    return wav_slices, mel_slices
-
-
-def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs):
-    """
-    Computes an embedding for a single utterance.
-
-    # TODO: handle multiple wavs to benefit from batching on GPU
-    :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
-    :param using_partials: if True, then the utterance is split in partial utterances of
-    <partial_utterance_n_frames> frames and the utterance embedding is computed from their
-    normalized average. If False, the utterance is instead computed from feeding the entire
-    spectogram to the network.
-    :param return_partials: if True, the partial embeddings will also be returned along with the
-    wav slices that correspond to the partial embeddings.
-    :param kwargs: additional arguments to compute_partial_splits()
-    :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
-    <return_partials> is True, the partial utterances as a numpy array of float32 of shape
-    (n_partials, model_embedding_size) and the wav partials as a list of slices will also be
-    returned. If <using_partials> is simultaneously set to False, both these values will be None
-    instead.
-    """
-    # Process the entire utterance if not using partials
-    if not using_partials:
-        frames = audio.wav_to_mel_spectrogram(wav)
-        embed = embed_frames_batch(frames[None, ...])[0]
-        if return_partials:
-            return embed, None, None
-        return embed
-
-    # Compute where to split the utterance into partials and pad if necessary
-    wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs)
-    max_wave_length = wave_slices[-1].stop
-    if max_wave_length >= len(wav):
-        wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
-
-    # Split the utterance into partials
-    frames = audio.wav_to_mel_spectrogram(wav)
-    frames_batch = np.array([frames[s] for s in mel_slices])
-    partial_embeds = embed_frames_batch(frames_batch)
-
-    # Compute the utterance embedding from the partial embeddings
-    raw_embed = np.mean(partial_embeds, axis=0)
-    embed = raw_embed / np.linalg.norm(raw_embed, 2)
-
-    if return_partials:
-        return embed, partial_embeds, wave_slices
-    return embed
-
-
-def embed_speaker(wavs, **kwargs):
-    raise NotImplemented()
-
-
-def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)):
-    import matplotlib.pyplot as plt
-    if ax is None:
-        ax = plt.gca()
-
-    if shape is None:
-        height = int(np.sqrt(len(embed)))
-        shape = (height, -1)
-    embed = embed.reshape(shape)
-
-    cmap = cm.get_cmap()
-    mappable = ax.imshow(embed, cmap=cmap)
-    cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04)
-    sm = cm.ScalarMappable(cmap=cmap)
-    sm.set_clim(*color_range)
-
-    ax.set_xticks([]), ax.set_yticks([])
-    ax.set_title(title)
diff --git a/encoder/model.py b/encoder/model.py
deleted file mode 100644
index e050d3204d8f1becdf0f8b3133470708e5420cea..0000000000000000000000000000000000000000
--- a/encoder/model.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from encoder.params_model import *
-from encoder.params_data import *
-from scipy.interpolate import interp1d
-from sklearn.metrics import roc_curve
-from torch.nn.utils import clip_grad_norm_
-from scipy.optimize import brentq
-from torch import nn
-import numpy as np
-import torch
-
-
-class SpeakerEncoder(nn.Module):
-    def __init__(self, device, loss_device):
-        super().__init__()
-        self.loss_device = loss_device
-        
-        # Network defition
-        self.lstm = nn.LSTM(input_size=mel_n_channels,
-                            hidden_size=model_hidden_size, 
-                            num_layers=model_num_layers, 
-                            batch_first=True).to(device)
-        self.linear = nn.Linear(in_features=model_hidden_size, 
-                                out_features=model_embedding_size).to(device)
-        self.relu = torch.nn.ReLU().to(device)
-        
-        # Cosine similarity scaling (with fixed initial parameter values)
-        self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
-        self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)
-
-        # Loss
-        self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
-        
-    def do_gradient_ops(self):
-        # Gradient scale
-        self.similarity_weight.grad *= 0.01
-        self.similarity_bias.grad *= 0.01
-            
-        # Gradient clipping
-        clip_grad_norm_(self.parameters(), 3, norm_type=2)
-    
-    def forward(self, utterances, hidden_init=None):
-        """
-        Computes the embeddings of a batch of utterance spectrograms.
-        
-        :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape 
-        (batch_size, n_frames, n_channels) 
-        :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, 
-        batch_size, hidden_size). Will default to a tensor of zeros if None.
-        :return: the embeddings as a tensor of shape (batch_size, embedding_size)
-        """
-        # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
-        # and the final cell state.
-        out, (hidden, cell) = self.lstm(utterances, hidden_init)
-        
-        # We take only the hidden state of the last layer
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        
-        # L2-normalize it
-        embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5)        
-
-        return embeds
-    
-    def similarity_matrix(self, embeds):
-        """
-        Computes the similarity matrix according the section 2.1 of GE2E.
-
-        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
-        utterances_per_speaker, embedding_size)
-        :return: the similarity matrix as a tensor of shape (speakers_per_batch,
-        utterances_per_speaker, speakers_per_batch)
-        """
-        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-        
-        # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
-        centroids_incl = torch.mean(embeds, dim=1, keepdim=True)
-        centroids_incl = centroids_incl.clone() / (torch.norm(centroids_incl, dim=2, keepdim=True) + 1e-5)
-
-        # Exclusive centroids (1 per utterance)
-        centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds)
-        centroids_excl /= (utterances_per_speaker - 1)
-        centroids_excl = centroids_excl.clone() / (torch.norm(centroids_excl, dim=2, keepdim=True) + 1e-5)
-
-        # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot
-        # product of these vectors (which is just an element-wise multiplication reduced by a sum).
-        # We vectorize the computation for efficiency.
-        sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,
-                                 speakers_per_batch).to(self.loss_device)
-        mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int)
-        for j in range(speakers_per_batch):
-            mask = np.where(mask_matrix[j])[0]
-            sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2)
-            sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1)
-        
-        ## Even more vectorized version (slower maybe because of transpose)
-        # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker
-        #                           ).to(self.loss_device)
-        # eye = np.eye(speakers_per_batch, dtype=np.int)
-        # mask = np.where(1 - eye)
-        # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2)
-        # mask = np.where(eye)
-        # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2)
-        # sim_matrix2 = sim_matrix2.transpose(1, 2)
-        
-        sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
-        return sim_matrix
-    
-    def loss(self, embeds):
-        """
-        Computes the softmax loss according the section 2.1 of GE2E.
-        
-        :param embeds: the embeddings as a tensor of shape (speakers_per_batch, 
-        utterances_per_speaker, embedding_size)
-        :return: the loss and the EER for this batch of embeddings.
-        """
-        speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-        
-        # Loss
-        sim_matrix = self.similarity_matrix(embeds)
-        sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, 
-                                         speakers_per_batch))
-        ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
-        target = torch.from_numpy(ground_truth).long().to(self.loss_device)
-        loss = self.loss_fn(sim_matrix, target)
-        
-        # EER (not backpropagated)
-        with torch.no_grad():
-            inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0]
-            labels = np.array([inv_argmax(i) for i in ground_truth])
-            preds = sim_matrix.detach().cpu().numpy()
-
-            # Snippet from https://yangcha.github.io/EER-ROC/
-            fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())           
-            eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
-            
-        return loss, eer
diff --git a/encoder/params_data.py b/encoder/params_data.py
deleted file mode 100644
index bdb1716ed45617f2b127a7fb8885afe6cc74fb71..0000000000000000000000000000000000000000
--- a/encoder/params_data.py
+++ /dev/null
@@ -1,29 +0,0 @@
-
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-# Number of spectrogram frames at inference
-inference_n_frames = 80     #  800 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
diff --git a/encoder/params_model.py b/encoder/params_model.py
deleted file mode 100644
index 3e356472fb5a27f370cb3920976a11d12a76c1b7..0000000000000000000000000000000000000000
--- a/encoder/params_model.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3
-
-
-## Training parameters
-learning_rate_init = 1e-4
-speakers_per_batch = 64
-utterances_per_speaker = 10
diff --git a/encoder/preprocess.py b/encoder/preprocess.py
deleted file mode 100644
index d2dfc5ecf0fda2e57c919285e65e28c1ab0dfa60..0000000000000000000000000000000000000000
--- a/encoder/preprocess.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from datetime import datetime
-from functools import partial
-from multiprocessing import Pool
-from pathlib import Path
-
-import numpy as np
-from tqdm import tqdm
-
-from encoder import audio
-from encoder.config import librispeech_datasets, anglophone_nationalites
-from encoder.params_data import *
-
-
-_AUDIO_EXTENSIONS = ("wav", "flac", "m4a", "mp3")
-
-class DatasetLog:
-    """
-    Registers metadata about the dataset in a text file.
-    """
-    def __init__(self, root, name):
-        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
-        self.sample_data = dict()
-
-        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Creating dataset %s on %s" % (name, start_time))
-        self.write_line("-----")
-        self._log_params()
-
-    def _log_params(self):
-        from encoder import params_data
-        self.write_line("Parameter values:")
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            self.write_line("\t%s: %s" % (param_name, value))
-        self.write_line("-----")
-
-    def write_line(self, line):
-        self.text_file.write("%s\n" % line)
-
-    def add_sample(self, **kwargs):
-        for param_name, value in kwargs.items():
-            if not param_name in self.sample_data:
-                self.sample_data[param_name] = []
-            self.sample_data[param_name].append(value)
-
-    def finalize(self):
-        self.write_line("Statistics:")
-        for param_name, values in self.sample_data.items():
-            self.write_line("\t%s:" % param_name)
-            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
-            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
-        self.write_line("-----")
-        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Finished on %s" % end_time)
-        self.text_file.close()
-
-
-def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
-    dataset_root = datasets_root.joinpath(dataset_name)
-    if not dataset_root.exists():
-        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
-        return None, None
-    return dataset_root, DatasetLog(out_dir, dataset_name)
-
-
-def _preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, skip_existing: bool):
-    # Give a name to the speaker that includes its dataset
-    speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-
-    # Create an output directory with that name, as well as a txt file containing a
-    # reference to each source file.
-    speaker_out_dir = out_dir.joinpath(speaker_name)
-    speaker_out_dir.mkdir(exist_ok=True)
-    sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-
-    # There's a possibility that the preprocessing was interrupted earlier, check if
-    # there already is a sources file.
-    if sources_fpath.exists():
-        try:
-            with sources_fpath.open("r") as sources_file:
-                existing_fnames = {line.split(",")[0] for line in sources_file}
-        except:
-            existing_fnames = {}
-    else:
-        existing_fnames = {}
-
-    # Gather all audio files for that speaker recursively
-    sources_file = sources_fpath.open("a" if skip_existing else "w")
-    audio_durs = []
-    for extension in _AUDIO_EXTENSIONS:
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-            audio_durs.append(len(wav) / sampling_rate)
-
-    sources_file.close()
-
-    return audio_durs
-
-
-def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger):
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-
-    # Process the utterances for each speaker
-    work_fn = partial(_preprocess_speaker, datasets_root=datasets_root, out_dir=out_dir, skip_existing=skip_existing)
-    with Pool(4) as pool:
-        tasks = pool.imap(work_fn, speaker_dirs)
-        for sample_durs in tqdm(tasks, dataset_name, len(speaker_dirs), unit="speakers"):
-            for sample_dur in sample_durs:
-                logger.add_sample(duration=sample_dur)
-
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-
-def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
-    for dataset_name in librispeech_datasets["train"]["other"]:
-        # Initialize the preprocessing
-        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-        if not dataset_root:
-            return
-
-        # Preprocess all speakers
-        speaker_dirs = list(dataset_root.glob("*"))
-        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
-
-
-def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb1"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the contents of the meta file
-    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
-        metadata = [line.split("\t") for line in metafile][1:]
-
-    # Select the ID and the nationality, filter out non-anglophone speakers
-    nationalities = {line[0]: line[3] for line in metadata}
-    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if
-                        nationality.lower() in anglophone_nationalites]
-    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." %
-          (len(keep_speaker_ids), len(nationalities)))
-
-    # Get the speaker directories for anglophone speakers only
-    speaker_dirs = dataset_root.joinpath("wav").glob("*")
-    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
-                    speaker_dir.name in keep_speaker_ids]
-    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." %
-          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
-
-    # Preprocess all speakers
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
-
-
-def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb2"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the speaker directories
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, skip_existing, logger)
diff --git a/encoder/train.py b/encoder/train.py
deleted file mode 100644
index 2bed4eb2f2f3e343b382a1b9cbf78a9ffb11c002..0000000000000000000000000000000000000000
--- a/encoder/train.py
+++ /dev/null
@@ -1,125 +0,0 @@
-from pathlib import Path
-
-import torch
-
-from encoder.data_objects import SpeakerVerificationDataLoader, SpeakerVerificationDataset
-from encoder.model import SpeakerEncoder
-from encoder.params_model import *
-from encoder.visualizations import Visualizations
-from utils.profiler import Profiler
-
-
-def sync(device: torch.device):
-    # For correct profiling (cuda operations are async)
-    if device.type == "cuda":
-        torch.cuda.synchronize(device)
-
-
-def train(run_id: str, clean_data_root: Path, models_dir: Path, umap_every: int, save_every: int,
-          backup_every: int, vis_every: int, force_restart: bool, visdom_server: str,
-          no_visdom: bool):
-    # Create a dataset and a dataloader
-    dataset = SpeakerVerificationDataset(clean_data_root)
-    loader = SpeakerVerificationDataLoader(
-        dataset,
-        speakers_per_batch,
-        utterances_per_speaker,
-        num_workers=4,
-    )
-
-    # Setup the device on which to run the forward pass and the loss. These can be different,
-    # because the forward pass is faster on the GPU whereas the loss is often (depending on your
-    # hyperparameters) faster on the CPU.
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    # FIXME: currently, the gradient is None if loss_device is cuda
-    loss_device = torch.device("cpu")
-
-    # Create the model and the optimizer
-    model = SpeakerEncoder(device, loss_device)
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate_init)
-    init_step = 1
-
-    # Configure file path for the model
-    model_dir = models_dir / run_id
-    model_dir.mkdir(exist_ok=True, parents=True)
-    state_fpath = model_dir / "encoder.pt"
-
-    # Load any existing model
-    if not force_restart:
-        if state_fpath.exists():
-            print("Found existing model \"%s\", loading it and resuming training." % run_id)
-            checkpoint = torch.load(state_fpath)
-            init_step = checkpoint["step"]
-            model.load_state_dict(checkpoint["model_state"])
-            optimizer.load_state_dict(checkpoint["optimizer_state"])
-            optimizer.param_groups[0]["lr"] = learning_rate_init
-        else:
-            print("No model \"%s\" found, starting training from scratch." % run_id)
-    else:
-        print("Starting the training from scratch.")
-    model.train()
-
-    # Initialize the visualization environment
-    vis = Visualizations(run_id, vis_every, server=visdom_server, disabled=no_visdom)
-    vis.log_dataset(dataset)
-    vis.log_params()
-    device_name = str(torch.cuda.get_device_name(0) if torch.cuda.is_available() else "CPU")
-    vis.log_implementation({"Device": device_name})
-
-    # Training loop
-    profiler = Profiler(summarize_every=10, disabled=False)
-    for step, speaker_batch in enumerate(loader, init_step):
-        profiler.tick("Blocking, waiting for batch (threaded)")
-
-        # Forward pass
-        inputs = torch.from_numpy(speaker_batch.data).to(device)
-        sync(device)
-        profiler.tick("Data to %s" % device)
-        embeds = model(inputs)
-        sync(device)
-        profiler.tick("Forward pass")
-        embeds_loss = embeds.view((speakers_per_batch, utterances_per_speaker, -1)).to(loss_device)
-        loss, eer = model.loss(embeds_loss)
-        sync(loss_device)
-        profiler.tick("Loss")
-
-        # Backward pass
-        model.zero_grad()
-        loss.backward()
-        profiler.tick("Backward pass")
-        model.do_gradient_ops()
-        optimizer.step()
-        profiler.tick("Parameter update")
-
-        # Update visualizations
-        # learning_rate = optimizer.param_groups[0]["lr"]
-        vis.update(loss.item(), eer, step)
-
-        # Draw projections and save them to the backup folder
-        if umap_every != 0 and step % umap_every == 0:
-            print("Drawing and saving projections (step %d)" % step)
-            projection_fpath = model_dir / f"umap_{step:06d}.png"
-            embeds = embeds.detach().cpu().numpy()
-            vis.draw_projections(embeds, utterances_per_speaker, step, projection_fpath)
-            vis.save()
-
-        # Overwrite the latest version of the model
-        if save_every != 0 and step % save_every == 0:
-            print("Saving the model (step %d)" % step)
-            torch.save({
-                "step": step + 1,
-                "model_state": model.state_dict(),
-                "optimizer_state": optimizer.state_dict(),
-            }, state_fpath)
-
-        # Make a backup
-        if backup_every != 0 and step % backup_every == 0:
-            print("Making a backup (step %d)" % step)
-            backup_fpath = model_dir / f"encoder_{step:06d}.bak"
-            torch.save({
-                "step": step + 1,
-                "model_state": model.state_dict(),
-                "optimizer_state": optimizer.state_dict(),
-            }, backup_fpath)
-
-        profiler.tick("Extras (visualizations, saving)")
diff --git a/encoder/visualizations.py b/encoder/visualizations.py
deleted file mode 100644
index d103944f262161a572148f81d141c9f06422ff41..0000000000000000000000000000000000000000
--- a/encoder/visualizations.py
+++ /dev/null
@@ -1,179 +0,0 @@
-from datetime import datetime
-from time import perf_counter as timer
-
-import numpy as np
-import umap
-import visdom
-
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-
-
-colormap = np.array([
-    [76, 255, 0],
-    [0, 127, 70],
-    [255, 0, 0],
-    [255, 217, 38],
-    [0, 135, 255],
-    [165, 0, 165],
-    [255, 167, 255],
-    [0, 255, 255],
-    [255, 96, 38],
-    [142, 76, 0],
-    [33, 0, 127],
-    [0, 0, 0],
-    [183, 183, 183],
-], dtype=np.float) / 255
-
-
-class Visualizations:
-    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
-        # Tracking data
-        self.last_update_timestamp = timer()
-        self.update_every = update_every
-        self.step_times = []
-        self.losses = []
-        self.eers = []
-        print("Updating the visualizations every %d steps." % update_every)
-
-        # If visdom is disabled TODO: use a better paradigm for that
-        self.disabled = disabled
-        if self.disabled:
-            return
-
-        # Set the environment name
-        now = str(datetime.now().strftime("%d-%m %Hh%M"))
-        if env_name is None:
-            self.env_name = now
-        else:
-            self.env_name = "%s (%s)" % (env_name, now)
-
-        # Connect to visdom and open the corresponding window in the browser
-        try:
-            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
-        except ConnectionError:
-            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
-                            "start it.")
-        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
-
-        # Create the windows
-        self.loss_win = None
-        self.eer_win = None
-        # self.lr_win = None
-        self.implementation_win = None
-        self.projection_win = None
-        self.implementation_string = ""
-
-    def log_params(self):
-        if self.disabled:
-            return
-        from encoder import params_data
-        from encoder import params_model
-        param_string = "<b>Model parameters</b>:<br>"
-        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
-            value = getattr(params_model, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        param_string += "<b>Data parameters</b>:<br>"
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        self.vis.text(param_string, opts={"title": "Parameters"})
-
-    def log_dataset(self, dataset: SpeakerVerificationDataset):
-        if self.disabled:
-            return
-        dataset_string = ""
-        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
-        dataset_string += "\n" + dataset.get_logs()
-        dataset_string = dataset_string.replace("\n", "<br>")
-        self.vis.text(dataset_string, opts={"title": "Dataset"})
-
-    def log_implementation(self, params):
-        if self.disabled:
-            return
-        implementation_string = ""
-        for param, value in params.items():
-            implementation_string += "<b>%s</b>: %s\n" % (param, value)
-            implementation_string = implementation_string.replace("\n", "<br>")
-        self.implementation_string = implementation_string
-        self.implementation_win = self.vis.text(
-            implementation_string,
-            opts={"title": "Training implementation"}
-        )
-
-    def update(self, loss, eer, step):
-        # Update the tracking data
-        now = timer()
-        self.step_times.append(1000 * (now - self.last_update_timestamp))
-        self.last_update_timestamp = now
-        self.losses.append(loss)
-        self.eers.append(eer)
-        print(".", end="")
-
-        # Update the plots every <update_every> steps
-        if step % self.update_every != 0:
-            return
-        time_string = "Step time:  mean: %5dms  std: %5dms" % \
-                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
-        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
-              (step, np.mean(self.losses), np.mean(self.eers), time_string))
-        if not self.disabled:
-            self.loss_win = self.vis.line(
-                [np.mean(self.losses)],
-                [step],
-                win=self.loss_win,
-                update="append" if self.loss_win else None,
-                opts=dict(
-                    legend=["Avg. loss"],
-                    xlabel="Step",
-                    ylabel="Loss",
-                    title="Loss",
-                )
-            )
-            self.eer_win = self.vis.line(
-                [np.mean(self.eers)],
-                [step],
-                win=self.eer_win,
-                update="append" if self.eer_win else None,
-                opts=dict(
-                    legend=["Avg. EER"],
-                    xlabel="Step",
-                    ylabel="EER",
-                    title="Equal error rate"
-                )
-            )
-            if self.implementation_win is not None:
-                self.vis.text(
-                    self.implementation_string + ("<b>%s</b>" % time_string),
-                    win=self.implementation_win,
-                    opts={"title": "Training implementation"},
-                )
-
-        # Reset the tracking
-        self.losses.clear()
-        self.eers.clear()
-        self.step_times.clear()
-
-    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None, max_speakers=10):
-        import matplotlib.pyplot as plt
-
-        max_speakers = min(max_speakers, len(colormap))
-        embeds = embeds[:max_speakers * utterances_per_speaker]
-
-        n_speakers = len(embeds) // utterances_per_speaker
-        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
-        colors = [colormap[i] for i in ground_truth]
-
-        reducer = umap.UMAP()
-        projected = reducer.fit_transform(embeds)
-        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
-        plt.gca().set_aspect("equal", "datalim")
-        plt.title("UMAP projection (step %d)" % step)
-        if not self.disabled:
-            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
-        if out_fpath is not None:
-            plt.savefig(out_fpath)
-        plt.clf()
-
-    def save(self):
-        if not self.disabled:
-            self.vis.save([self.env_name])
diff --git a/encoderCoren.pt b/encoderCoren.pt
deleted file mode 100644
index 5cd2e41ea79e4aeb8414c7ed9993c42ab5b0dc28..0000000000000000000000000000000000000000
--- a/encoderCoren.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:39373b86598fa3da9fcddee6142382efe09777e8d37dc9c0561f41f0070f134e
-size 17090379
diff --git a/hifigan/LICENSE b/hifi-gan/LICENSE
similarity index 100%
rename from hifigan/LICENSE
rename to hifi-gan/LICENSE
diff --git a/hifi-gan/README.md b/hifi-gan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4c63381f7c41874fe97c13f40b92d6bb9413e751
--- /dev/null
+++ b/hifi-gan/README.md
@@ -0,0 +1,105 @@
+# HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
+
+### Jungil Kong, Jaehyeon Kim, Jaekyoung Bae
+
+In our [paper](https://arxiv.org/abs/2010.05646), 
+we proposed HiFi-GAN: a GAN-based model capable of generating high fidelity speech efficiently.<br/>
+We provide our implementation and pretrained models as open source in this repository.
+
+**Abstract :**
+Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. 
+Although such methods improve the sampling efficiency and memory usage, 
+their sample quality has not yet reached that of autoregressive and flow-based generative models. 
+In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. 
+As speech audio consists of sinusoidal signals with various periods, 
+we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. 
+A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method 
+demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than 
+real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen 
+speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times 
+faster than real-time on CPU with comparable quality to an autoregressive counterpart.
+
+Visit our [demo website](https://jik876.github.io/hifi-gan-demo/) for audio samples.
+
+
+## Pre-requisites
+1. Python >= 3.6
+2. Clone this repository.
+3. Install python requirements. Please refer [requirements.txt](requirements.txt)
+4. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/).
+And move all wav files to `LJSpeech-1.1/wavs`
+
+
+## Training
+```
+python train.py --config config_v1.json
+```
+To train V2 or V3 Generator, replace `config_v1.json` with `config_v2.json` or `config_v3.json`.<br>
+Checkpoints and copy of the configuration file are saved in `cp_hifigan` directory by default.<br>
+You can change the path by adding `--checkpoint_path` option.
+
+Validation loss during training with V1 generator.<br>
+![validation loss](./validation_loss.png)
+
+## Pretrained Model
+You can also use pretrained models we provide.<br/>
+[Download pretrained models](https://drive.google.com/drive/folders/1-eEYTB5Av9jNql0WGBlRoi-WH2J7bp5Y?usp=sharing)<br/> 
+Details of each folder are as in follows:
+
+|Folder Name|Generator|Dataset|Fine-Tuned|
+|------|---|---|---|
+|LJ_V1|V1|LJSpeech|No|
+|LJ_V2|V2|LJSpeech|No|
+|LJ_V3|V3|LJSpeech|No|
+|LJ_FT_T2_V1|V1|LJSpeech|Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2))|
+|LJ_FT_T2_V2|V2|LJSpeech|Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2))|
+|LJ_FT_T2_V3|V3|LJSpeech|Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2))|
+|VCTK_V1|V1|VCTK|No|
+|VCTK_V2|V2|VCTK|No|
+|VCTK_V3|V3|VCTK|No|
+|UNIVERSAL_V1|V1|Universal|No|
+
+We provide the universal model with discriminator weights that can be used as a base for transfer learning to other datasets.
+
+## Fine-Tuning
+1. Generate mel-spectrograms in numpy format using [Tacotron2](https://github.com/NVIDIA/tacotron2) with teacher-forcing.<br/>
+The file name of the generated mel-spectrogram should match the audio file and the extension should be `.npy`.<br/>
+Example:
+    ```
+    Audio File : LJ001-0001.wav
+    Mel-Spectrogram File : LJ001-0001.npy
+    ```
+2. Create `ft_dataset` folder and copy the generated mel-spectrogram files into it.<br/>
+3. Run the following command.
+    ```
+    python train.py --fine_tuning True --config config_v1.json
+    ```
+    For other command line options, please refer to the training section.
+
+
+## Inference from wav file
+1. Make `test_files` directory and copy wav files into the directory.
+2. Run the following command.
+    ```
+    python inference.py --checkpoint_file [generator checkpoint file path]
+    ```
+Generated wav files are saved in `generated_files` by default.<br>
+You can change the path by adding `--output_dir` option.
+
+
+## Inference for end-to-end speech synthesis
+1. Make `test_mel_files` directory and copy generated mel-spectrogram files into the directory.<br>
+You can generate mel-spectrograms using [Tacotron2](https://github.com/NVIDIA/tacotron2), 
+[Glow-TTS](https://github.com/jaywalnut310/glow-tts) and so forth.
+2. Run the following command.
+    ```
+    python inference_e2e.py --checkpoint_file [generator checkpoint file path]
+    ```
+Generated wav files are saved in `generated_files_from_mel` by default.<br>
+You can change the path by adding `--output_dir` option.
+
+
+## Acknowledgements
+We referred to [WaveGlow](https://github.com/NVIDIA/waveglow), [MelGAN](https://github.com/descriptinc/melgan-neurips) 
+and [Tacotron2](https://github.com/NVIDIA/tacotron2) to implement this.
+
diff --git a/diagrams/apple.txt b/hifi-gan/apple.py
similarity index 100%
rename from diagrams/apple.txt
rename to hifi-gan/apple.py
diff --git a/hifigan/env.py b/hifi-gan/env.py
similarity index 100%
rename from hifigan/env.py
rename to hifi-gan/env.py
diff --git a/hifigan/inference.py b/hifi-gan/inference.py
similarity index 94%
rename from hifigan/inference.py
rename to hifi-gan/inference.py
index 10ed36f1d8aa6d1d169333ba1553162a40d5bf0c..96ba10672fd808d9d5f5be673bd58f2db7878eda 100644
--- a/hifigan/inference.py
+++ b/hifi-gan/inference.py
@@ -6,9 +6,9 @@ import argparse
 import json
 import torch
 from scipy.io.wavfile import write
-from hifigan.env import AttrDict
-from hifigan.meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
-from hifigan.models import Generator
+from env import AttrDict
+from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
+from models import Generator
 
 h = None
 device = None
diff --git a/hifi-gan/inference_e2e.py b/hifi-gan/inference_e2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a8b6b32306274fbe08f8023d1b21e59532e3b7d
--- /dev/null
+++ b/hifi-gan/inference_e2e.py
@@ -0,0 +1,90 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import glob
+import os
+import numpy as np
+import argparse
+import json
+import torch
+from scipy.io.wavfile import write
+from env import AttrDict
+from meldataset import MAX_WAV_VALUE
+from models import Generator
+
+h = None
+device = None
+
+
+def load_checkpoint(filepath, device):
+    assert os.path.isfile(filepath)
+    print("Loading '{}'".format(filepath))
+    checkpoint_dict = torch.load(filepath, map_location=device)
+    print("Complete.")
+    return checkpoint_dict
+
+
+def scan_checkpoint(cp_dir, prefix):
+    pattern = os.path.join(cp_dir, prefix + '*')
+    cp_list = glob.glob(pattern)
+    if len(cp_list) == 0:
+        return ''
+    return sorted(cp_list)[-1]
+
+
+def inference(a):
+    generator = Generator(h).to(device)
+
+    state_dict_g = load_checkpoint(a.checkpoint_file, device)
+    generator.load_state_dict(state_dict_g['generator'])
+
+    filelist = os.listdir(a.input_mels_dir)
+
+    os.makedirs(a.output_dir, exist_ok=True)
+
+    generator.eval()
+    generator.remove_weight_norm()
+    with torch.no_grad():
+        for i, filname in enumerate(filelist):
+            x = np.load(os.path.join(a.input_mels_dir, filname))
+            x = torch.FloatTensor(x).to(device)
+            y_g_hat = generator(x)
+            audio = y_g_hat.squeeze()
+            audio = audio * MAX_WAV_VALUE
+            audio = audio.cpu().numpy().astype('int16')
+
+            output_file = os.path.join(a.output_dir, os.path.splitext(filname)[0] + '_generated_e2e.wav')
+            write(output_file, h.sampling_rate, audio)
+            print(output_file)
+
+
+def main():
+    print('Initializing Inference Process..')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_mels_dir', default='test_mel_files')
+    parser.add_argument('--output_dir', default='generated_files_from_mel')
+    parser.add_argument('--checkpoint_file', required=True)
+    a = parser.parse_args()
+
+    config_file = os.path.join(os.path.split(a.checkpoint_file)[0], 'config.json')
+    with open(config_file) as f:
+        data = f.read()
+
+    global h
+    json_config = json.loads(data)
+    h = AttrDict(json_config)
+
+    torch.manual_seed(h.seed)
+    global device
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(h.seed)
+        device = torch.device('cuda')
+    else:
+        device = torch.device('cpu')
+
+    inference(a)
+
+
+if __name__ == '__main__':
+    main()
+
diff --git a/hifigan/meldataset.py b/hifi-gan/meldataset.py
similarity index 100%
rename from hifigan/meldataset.py
rename to hifi-gan/meldataset.py
diff --git a/hifigan/models.py b/hifi-gan/models.py
similarity index 99%
rename from hifigan/models.py
rename to hifi-gan/models.py
index da5cf65e8c7518e60b0bf23889d94b69e639a310..da233d02d4df993c101277506f7ac6e1286fb8bd 100644
--- a/hifigan/models.py
+++ b/hifi-gan/models.py
@@ -3,7 +3,7 @@ import torch.nn.functional as F
 import torch.nn as nn
 from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
 from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from hifigan.hifigan_utils import init_weights, get_padding
+from utils import init_weights, get_padding
 
 LRELU_SLOPE = 0.1
 
diff --git a/hifi-gan/requirements.txt b/hifi-gan/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..60c193b0a88d9e733cec5516385c41262c68eb62
--- /dev/null
+++ b/hifi-gan/requirements.txt
@@ -0,0 +1,7 @@
+torch==1.4.0
+numpy==1.17.4
+librosa==0.7.2
+scipy==1.4.1
+tensorboard==2.0
+soundfile==0.10.3.post1
+matplotlib==3.1.3
\ No newline at end of file
diff --git a/hifigan/train.py b/hifi-gan/train.py
similarity index 97%
rename from hifigan/train.py
rename to hifi-gan/train.py
index bc00fed4002cc79d47cd960f4d6c41af7adbc153..3b55094289d8649b5cde18f9675700d1f6ab5ba9 100644
--- a/hifigan/train.py
+++ b/hifi-gan/train.py
@@ -12,11 +12,11 @@ from torch.utils.data import DistributedSampler, DataLoader
 import torch.multiprocessing as mp
 from torch.distributed import init_process_group
 from torch.nn.parallel import DistributedDataParallel
-from hifigan.env import AttrDict, build_env
-from hifigan.meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
-from hifigan.models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
+from env import AttrDict, build_env
+from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist
+from models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\
     discriminator_loss
-from hifigan.hifigan_utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
+from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint
 
 torch.backends.cudnn.benchmark = True
 
diff --git a/hifigan/hifigan_utils.py b/hifi-gan/utils.py
similarity index 100%
rename from hifigan/hifigan_utils.py
rename to hifi-gan/utils.py
diff --git a/hifigan/inference_e2e.py b/hifigan/inference_e2e.py
deleted file mode 100644
index 6115600faf757859fdf99d1691ea3d353ee74689..0000000000000000000000000000000000000000
--- a/hifigan/inference_e2e.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import os
-import numpy as np
-import json
-import torch
-from scipy.io.wavfile import write
-from hifigan.env import AttrDict
-from hifigan.models import Generator
-from io import BytesIO
-
-h = None
-device = None
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def hifi_gan_inference(input_mel, checkpoint_file):
-    print('Initializing Inference Process..')
-    config_file = os.path.join(os.path.split(checkpoint_file)[0], 'config.json')
-    with open(config_file) as f:
-        data = f.read()
-
-    global h
-    json_config = json.loads(data)
-    h = AttrDict(json_config)
-
-    # Set MAX_WAV_VALUE if not present
-    if 'MAX_WAV_VALUE' not in h:
-        h.MAX_WAV_VALUE = 32768.0  # Adjust this value based on your requirements
-
-    torch.manual_seed(h.seed)
-    global device
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(h.seed)
-        device = torch.device('cuda')
-    else:
-        device = torch.device('cpu')
-
-    generator = Generator(h).to(device)
-
-    state_dict_g = load_checkpoint(checkpoint_file, device)
-    generator.load_state_dict(state_dict_g['generator'])
-
-    generator.eval()
-    generator.remove_weight_norm()
-
-    # Load data from BytesIO
-    buffer = BytesIO(input_mel)
-    x = np.load(buffer)
-
-    x = torch.FloatTensor(x).to(device)
-    y_g_hat = generator(x)
-    
-    # Detach tensor before converting to numpy
-    audio = y_g_hat.squeeze().detach().numpy()
-    
-    # Set MAX_WAV_VALUE if not present
-    if 'MAX_WAV_VALUE' not in h:
-        h.MAX_WAV_VALUE = 32768.0  # Adjust this value based on your requirements
-
-    audio = audio * h.MAX_WAV_VALUE
-    audio = audio.astype('int16')
-
-    # Save audio to BytesIO
-    output_buffer = BytesIO()
-    write(output_buffer, h.sampling_rate, audio)
-    
-    return output_buffer.getvalue()
-    
\ No newline at end of file
diff --git a/hparams.py b/hparams.py
index 86241ebf19fb57cdfdb48a7f051c5d433a92279e..3dc0ad7108094642cf2a542f715b6fc05987d2da 100644
--- a/hparams.py
+++ b/hparams.py
@@ -61,7 +61,6 @@ def create_hparams(hparams_string=None, verbose=False):
         "encoder_kernel_size":5,
         "encoder_n_convolutions":3,
         "encoder_embedding_dim":512,
-        "speaker_embedding_dim":256,
 
         # Decoder parameters
         "n_frames_per_step":1,  # currently only 1 is supported
diff --git a/kaggle_12000.pt b/kaggle_12000.pt
deleted file mode 100644
index 37f8cda2079aaf7c09065127cb749a04ac406374..0000000000000000000000000000000000000000
--- a/kaggle_12000.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27d4936bff68d3fe37053ec3110486bdea9f23bf137f07477c28bbd4f36b85ae
-size 338426303
diff --git a/logger.py b/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad327383a24484476801ea7f6d840b9fdb49786b
--- /dev/null
+++ b/logger.py
@@ -0,0 +1,48 @@
+import random
+import torch
+from torch.utils.tensorboard import SummaryWriter
+from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
+from plotting_utils import plot_gate_outputs_to_numpy
+
+
+class Tacotron2Logger(SummaryWriter):
+    def __init__(self, logdir):
+        super(Tacotron2Logger, self).__init__(logdir)
+
+    def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
+                     iteration):
+            self.add_scalar("training.loss", reduced_loss, iteration)
+            self.add_scalar("grad.norm", grad_norm, iteration)
+            self.add_scalar("learning.rate", learning_rate, iteration)
+            self.add_scalar("duration", duration, iteration)
+
+    def log_validation(self, reduced_loss, model, y, y_pred, iteration):
+        self.add_scalar("validation.loss", reduced_loss, iteration)
+        _, mel_outputs, gate_outputs, alignments = y_pred
+        mel_targets, gate_targets = y
+
+        # plot distribution of parameters
+        for tag, value in model.named_parameters():
+            tag = tag.replace('.', '/')
+            self.add_histogram(tag, value.data.cpu().numpy(), iteration)
+
+        # plot alignment, mel target and predicted, gate target and predicted
+        idx = random.randint(0, alignments.size(0) - 1)
+        self.add_image(
+            "alignment",
+            plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T),
+            iteration, dataformats='HWC')
+        self.add_image(
+            "mel_target",
+            plot_spectrogram_to_numpy(mel_targets[idx].data.cpu().numpy()),
+            iteration, dataformats='HWC')
+        self.add_image(
+            "mel_predicted",
+            plot_spectrogram_to_numpy(mel_outputs[idx].data.cpu().numpy()),
+            iteration, dataformats='HWC')
+        self.add_image(
+            "gate",
+            plot_gate_outputs_to_numpy(
+                gate_targets[idx].data.cpu().numpy(),
+                torch.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
+            iteration, dataformats='HWC')
diff --git a/logic.py b/logic.py
index 2a19ebbff2103878889e0c90ca9292545f9913ee..a76bdcb3f549dc222df96671ae416b71e7ecb9c8 100644
--- a/logic.py
+++ b/logic.py
@@ -3,28 +3,15 @@ import numpy as np
 import torch
 import base64
 import io
-from io import BytesIO
 import matplotlib.pyplot as plt
 from hparams import create_hparams
 from model import Tacotron2
-from layers import TacotronSTFT
 from train import load_model
 from text import text_to_sequence
-from utils import load_wav_to_torch
 import os
-import random
-import librosa
+import subprocess
 import librosa.display
 
-use_cuda = torch.cuda.is_available()
-device = torch.device('cuda' if use_cuda else 'cpu')
-
-hparams = create_hparams()
-hparams.sampling_rate = 22050
-stft = TacotronSTFT(
-    hparams.filter_length, hparams.hop_length, hparams.win_length, hparams.n_mel_channels, 
-    hparams.sampling_rate, hparams.mel_fmin, hparams.mel_fmax).to(device)
-
 # Function to plot data
 def plot_data(data, figsize=(16, 4), titles=['Mel Spectrogram (Original)', 'Mel Spectrogram (Postnet)', 'Alignment'],
               xlabel=['Time Steps', 'Time Steps', 'Decoder Time Steps'],
@@ -55,84 +42,59 @@ def plot_data(data, figsize=(16, 4), titles=['Mel Spectrogram (Original)', 'Mel
     return img_base64
 
 #Function to plot timedomain waveform
-def plot_waveforms(audio_data):
-    # Load the audio from BytesIO
-    buffer = BytesIO(audio_data)
-    y, sr = librosa.load(buffer, sr=None)
-
-    # Create waveform plot
-    plt.figure(figsize=(10, 4))
-    librosa.display.waveshow(y, sr=sr)
-    plt.xlabel("Time (s)")
-    plt.ylabel("Amplitude")
-    plt.title("Waveform")
-
-    # Save the plot to a BytesIO object
-    wave_buffer = BytesIO()
-    plt.savefig(wave_buffer, format="png")
-    wave_buffer.seek(0)
-    plt.close()
-
-    # Encode the plot as base64
-    wave_base64 = base64.b64encode(wave_buffer.read()).decode('utf-8')
-
-    return wave_base64
+def plot_waveforms(audio_file, sr=22050):
+    # Load audio waveform
+    y, sr = librosa.load(audio_file, sr=sr)
 
+    # Create time vector
+    time = librosa.times_like(y, sr=sr)
 
-# load speaker model
-def load_speaker_model(speaker_model_path):
-    from speaker.model import SpeakerEncoder
-    device = torch.device('cuda' if use_cuda else 'cpu')
-    loss_device = torch.device("cpu")
-
-    model = SpeakerEncoder(device, loss_device)
-    speaker_dict = torch.load(speaker_model_path, map_location='cpu')
-    model.load_state_dict(speaker_dict)
+    # Plot the waveform
+    plt.figure(figsize=(16, 4))
+    librosa.display.waveshow(y, sr=sr)
+    plt.title('Time vs Amplitude')
+    plt.xlabel('Time (s)')
+    plt.ylabel('Amplitude')
 
-    # Freeze the weights of the speaker model
-    for param in model.parameters():
-        param.requires_grad = False
+    plt.tight_layout()
+    # plt.savefig('static/waveform.png')
+    img_buffer = io.BytesIO()
+    plt.savefig(img_buffer, format='png', bbox_inches='tight', pad_inches=0)
+    plt.close()
 
-    return model
+    img_base64 = base64.b64encode(img_buffer.getvalue()).decode('utf-8')
     
-speaker_model = load_speaker_model('speaker/saved_models/saved_model_e273_LargeBatch.pt').to(device).eval().float()
+    return img_base64
 
-def extract_speech_embedding(audio_path: str):
-    audio, sampling_rate = load_wav_to_torch(audio_path)
-    if sampling_rate != stft.sampling_rate:
-        raise ValueError("{} SR doesn't match target {} SR".format(sampling_rate, stft.sampling_rate))
-    
-    audio_norm = audio / 32768.0
-    audio_norm = audio_norm.unsqueeze(0)
-    audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False).to(device)
-    melspec = stft.mel_spectrogram(audio_norm).transpose(1,2).float()
-
-    if melspec.shape[1] <= 128:
-        mel_slice = mel
-    else:
-        slice_start = random.randint(0,melspec.shape[1]-128)
-        mel_slice = melspec[:,slice_start:slice_start+128]
-    speaker_embedding = speaker_model(mel_slice)
-    return speaker_embedding
-    
 def synthesize_voice(text_input, checkpoint_path):
-    # Load Tacotron2 model from checkpoint
-    model = load_model(hparams)
-    checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
-    model.load_state_dict(checkpoint['state_dict'])
-    model = model.to(device).eval().float()
+    # Load Tacotron2 model
+    hparams = create_hparams()
+    hparams.sampling_rate = 22050
 
+    # Load model from checkpoint
+    model = load_model(hparams)
+    model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
+    model = model.cuda().eval().half()
 
     # Nepali text
-    speaker_audio_path='speaker_audio/ariana.wav'
     sequence = np.array(text_to_sequence(text_input, ['transliteration_cleaners']))[None, :]
-    sequence = torch.autograd.Variable(torch.from_numpy(sequence)).to(device).long()
-    speaker_embedding = extract_speech_embedding(speaker_audio_path)
-    
+    sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
+
     # Melspectrogram and Alignment graph
-    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence, speaker_embedding)
+    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
     mel_output_data = mel_outputs.data.cpu().numpy()[0]
     mel_output_postnet_data = mel_outputs_postnet.data.cpu().numpy()[0]
     alignments_data = alignments.data.cpu().numpy()[0].T
 
-    return mel_output_data, mel_output_postnet_data, alignments_data
\ No newline at end of file
+    np.save('mel_files/mel1'+'.npy', mel_output_data)
+
+    input_mels_dir = 'mel_files/'
+    output_dir = 'audio_output/'
+    run_hifigan_inference(input_mels_dir, output_dir)
+
+    return mel_output_data, mel_output_postnet_data, alignments_data
+
+
+def run_hifigan_inference(input_mels_dir, output_dir):
+    script_path = os.path.join(os.path.dirname("hifigan/"), "inference_e2e.py")  # Assuming both scripts are in the same directory
+    subprocess.run(["python", script_path, "--checkpoint_file", "generator_v1", "--input_mels_dir", input_mels_dir, "--output_dir", output_dir])
\ No newline at end of file
diff --git a/loss_function.py b/loss_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..99cae952bbc38cb5d89a5686b07a8dd458725e22
--- /dev/null
+++ b/loss_function.py
@@ -0,0 +1,19 @@
+from torch import nn
+
+
+class Tacotron2Loss(nn.Module):
+    def __init__(self):
+        super(Tacotron2Loss, self).__init__()
+
+    def forward(self, model_output, targets):
+        mel_target, gate_target = targets[0], targets[1]
+        mel_target.requires_grad = False
+        gate_target.requires_grad = False
+        gate_target = gate_target.view(-1, 1)
+
+        mel_out, mel_out_postnet, gate_out, _ = model_output
+        gate_out = gate_out.view(-1, 1)
+        mel_loss = nn.MSELoss()(mel_out, mel_target) + \
+            nn.MSELoss()(mel_out_postnet, mel_target)
+        gate_loss = nn.BCEWithLogitsLoss()(gate_out, gate_target)
+        return mel_loss + gate_loss
diff --git a/loss_scaler.py b/loss_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..88cc9cf29d3ce122d7582020b73be4f4a5641fcd
--- /dev/null
+++ b/loss_scaler.py
@@ -0,0 +1,131 @@
+import torch
+
+class LossScaler:
+
+    def __init__(self, scale=1):
+        self.cur_scale = scale
+
+    # `params` is a list / generator of torch.Variable
+    def has_overflow(self, params):
+        return False
+
+    # `x` is a torch.Tensor
+    def _has_inf_or_nan(x):
+        return False
+
+    # `overflow` is boolean indicating whether we overflowed in gradient
+    def update_scale(self, overflow):
+        pass
+
+    @property
+    def loss_scale(self):
+        return self.cur_scale
+
+    def scale_gradient(self, module, grad_in, grad_out):
+        return tuple(self.loss_scale * g for g in grad_in)
+
+    def backward(self, loss):
+        scaled_loss = loss*self.loss_scale
+        scaled_loss.backward()
+
+class DynamicLossScaler:
+
+    def __init__(self,
+                 init_scale=2**32,
+                 scale_factor=2.,
+                 scale_window=1000):
+        self.cur_scale = init_scale
+        self.cur_iter = 0
+        self.last_overflow_iter = -1
+        self.scale_factor = scale_factor
+        self.scale_window = scale_window
+
+    # `params` is a list / generator of torch.Variable
+    def has_overflow(self, params):
+#        return False
+        for p in params:
+            if p.grad is not None and DynamicLossScaler._has_inf_or_nan(p.grad.data):
+                return True
+
+        return False
+
+    # `x` is a torch.Tensor
+    def _has_inf_or_nan(x):
+        cpu_sum = float(x.float().sum())
+        if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
+            return True
+        return False
+
+    # `overflow` is boolean indicating whether we overflowed in gradient
+    def update_scale(self, overflow):
+        if overflow:
+            #self.cur_scale /= self.scale_factor
+            self.cur_scale = max(self.cur_scale/self.scale_factor, 1)
+            self.last_overflow_iter = self.cur_iter
+        else:
+            if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0:
+                self.cur_scale *= self.scale_factor
+#        self.cur_scale = 1
+        self.cur_iter += 1
+
+    @property
+    def loss_scale(self):
+        return self.cur_scale
+
+    def scale_gradient(self, module, grad_in, grad_out):
+        return tuple(self.loss_scale * g for g in grad_in)
+
+    def backward(self, loss):
+        scaled_loss = loss*self.loss_scale
+        scaled_loss.backward()
+
+##############################################################
+# Example usage below here -- assuming it's in a separate file
+##############################################################
+if __name__ == "__main__":
+    import torch
+    from torch.autograd import Variable
+    from dynamic_loss_scaler import DynamicLossScaler
+
+    # N is batch size; D_in is input dimension;
+    # H is hidden dimension; D_out is output dimension.
+    N, D_in, H, D_out = 64, 1000, 100, 10
+
+    # Create random Tensors to hold inputs and outputs, and wrap them in Variables.
+    x = Variable(torch.randn(N, D_in), requires_grad=False)
+    y = Variable(torch.randn(N, D_out), requires_grad=False)
+
+    w1 = Variable(torch.randn(D_in, H), requires_grad=True)
+    w2 = Variable(torch.randn(H, D_out), requires_grad=True)
+    parameters = [w1, w2]
+
+    learning_rate = 1e-6
+    optimizer = torch.optim.SGD(parameters, lr=learning_rate)
+    loss_scaler = DynamicLossScaler()
+
+    for t in range(500):
+        y_pred = x.mm(w1).clamp(min=0).mm(w2)
+        loss = (y_pred - y).pow(2).sum() * loss_scaler.loss_scale
+        print('Iter {} loss scale: {}'.format(t, loss_scaler.loss_scale))
+        print('Iter {} scaled loss: {}'.format(t, loss.data[0]))
+        print('Iter {} unscaled loss: {}'.format(t, loss.data[0] / loss_scaler.loss_scale))
+
+        # Run backprop
+        optimizer.zero_grad()
+        loss.backward()
+
+        # Check for overflow
+        has_overflow = DynamicLossScaler.has_overflow(parameters)
+
+        # If no overflow, unscale grad and update as usual
+        if not has_overflow:
+            for param in parameters:
+                param.grad.data.mul_(1. / loss_scaler.loss_scale)
+            optimizer.step()
+        # Otherwise, don't do anything -- ie, skip iteration
+        else:
+            print('OVERFLOW!')
+
+        # Update loss scale for next iteration
+        loss_scaler.update_scale(has_overflow)
+
diff --git a/model.py b/model.py
index 81315aa0958035bb11a1e7679e9c31cd8bb9e04e..ec0e9cea43e1b23f89934d85298b8f9d1883d13f 100644
--- a/model.py
+++ b/model.py
@@ -147,8 +147,13 @@ class Postnet(nn.Module):
 
 
 class Encoder(nn.Module):
+    """Encoder module:
+        - Three 1-d convolution banks
+        - Bidirectional LSTM
+    """
     def __init__(self, hparams):
         super(Encoder, self).__init__()
+
         convolutions = []
         for _ in range(hparams.encoder_n_convolutions):
             conv_layer = nn.Sequential(
@@ -165,15 +170,13 @@ class Encoder(nn.Module):
                             int(hparams.encoder_embedding_dim / 2), 1,
                             batch_first=True, bidirectional=True)
 
-    def forward(self, x, input_lengths, speaker_embedding):
-        # Modify the input x to concatenate the speaker embedding
-        x = torch.cat((x, speaker_embedding.unsqueeze(1).expand(-1, x.size(1), -1)), dim=-1)
-        
+    def forward(self, x, input_lengths):
         for conv in self.convolutions:
             x = F.dropout(F.relu(conv(x)), 0.5, self.training)
 
         x = x.transpose(1, 2)
 
+        # pytorch tensor are not reversible, hence the conversion
         input_lengths = input_lengths.cpu().numpy()
         x = nn.utils.rnn.pack_padded_sequence(
             x, input_lengths, batch_first=True)
@@ -186,10 +189,7 @@ class Encoder(nn.Module):
 
         return outputs
 
-    def inference(self, x, speaker_embedding):
-        # Modify the input x to concatenate the speaker embedding
-        x = torch.cat((x, speaker_embedding.unsqueeze(1).expand(-1, x.size(1), -1)), dim=-1)
-        
+    def inference(self, x):
         for conv in self.convolutions:
             x = F.dropout(F.relu(conv(x)), 0.5, self.training)
 
@@ -496,14 +496,13 @@ class Tacotron2(nn.Module):
 
         return outputs
 
-    def forward(self, inputs, speaker_embedding):
+    def forward(self, inputs):
         text_inputs, text_lengths, mels, max_len, output_lengths = inputs
         text_lengths, output_lengths = text_lengths.data, output_lengths.data
 
         embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
 
-        # Pass the speaker embedding to the Encoder
-        encoder_outputs = self.encoder(embedded_inputs, text_lengths, speaker_embedding)
+        encoder_outputs = self.encoder(embedded_inputs, text_lengths)
 
         mel_outputs, gate_outputs, alignments = self.decoder(
             encoder_outputs, mels, memory_lengths=text_lengths)
@@ -515,11 +514,9 @@ class Tacotron2(nn.Module):
             [mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
             output_lengths)
 
-    def inference(self, inputs, speaker_embedding):
+    def inference(self, inputs):
         embedded_inputs = self.embedding(inputs).transpose(1, 2)
-        # Pass the speaker embedding to the Encoder
-        encoder_outputs = self.encoder.inference(embedded_inputs, speaker_embedding)
-
+        encoder_outputs = self.encoder.inference(embedded_inputs)
         mel_outputs, gate_outputs, alignments = self.decoder.inference(
             encoder_outputs)
 
@@ -530,4 +527,3 @@ class Tacotron2(nn.Module):
             [mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
 
         return outputs
-
diff --git a/multiproc.py b/multiproc.py
new file mode 100644
index 0000000000000000000000000000000000000000..060ff937ace6c4170f12189e442c65f5093e0ecf
--- /dev/null
+++ b/multiproc.py
@@ -0,0 +1,23 @@
+import time
+import torch
+import sys
+import subprocess
+
+argslist = list(sys.argv)[1:]
+num_gpus = torch.cuda.device_count()
+argslist.append('--n_gpus={}'.format(num_gpus))
+workers = []
+job_id = time.strftime("%Y_%m_%d-%H%M%S")
+argslist.append("--group_name=group_{}".format(job_id))
+
+for i in range(num_gpus):
+    argslist.append('--rank={}'.format(i))
+    stdout = None if i == 0 else open("logs/{}_GPU_{}.log".format(job_id, i),
+                                      "w")
+    print(argslist)
+    p = subprocess.Popen([str(sys.executable)]+argslist, stdout=stdout)
+    workers.append(p)
+    argslist = argslist[:-1]
+
+for p in workers:
+    p.wait()
diff --git a/requirements.txt b/requirements.txt
index b43410d5e0938ea091ecae2bcc47a5c94a861541..5337500a34e2b98c889212433827437e666ad165 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,11 @@
-fastapi[all]
+flask
+flask_cors
+typing
+fastapi
 gunicorn
-torch==1.12.1+cu113
-torchaudio==0.12.1+cu113
-torchvision==0.13.1+cu113
+torch==1.12.1
+torchaudio==0.12.1
+torchvision==0.13.1
 matplotlib==3.5.3
 numpy==1.18.5
 inflect
@@ -11,6 +14,4 @@ scipy==1.7.3
 tensorboard==2.11.2
 Unidecode
 pillow
-uvicorn
-httpx==0.19.0
---extra-index-url https://download.pytorch.org/whl/cu113
\ No newline at end of file
+uvicorn
\ No newline at end of file
diff --git a/saved_model.pt b/saved_model.pt
deleted file mode 100644
index 190dc8b2d33332fbcc4cdf137aed68cdb654f903..0000000000000000000000000000000000000000
--- a/saved_model.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6ccc0abcd0fb77104be73e6675454a06e7797bf1d4a1177181c32b648e9d75a9
-size 5697243
diff --git a/speaker/__init__.py b/speaker/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/speaker/bana.txt b/speaker/bana.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/speaker/data.py b/speaker/data.py
deleted file mode 100644
index ccdf63399b39d0b6e26fb5e6c40b8b57bd4eb066..0000000000000000000000000000000000000000
--- a/speaker/data.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-import torchaudio.datasets as datasets
-import torchaudio.transforms as transforms
-from collections import defaultdict
-import random
-import layers
-
-import warnings
-
-class SpeakerMelLoader(torch.utils.data.Dataset):
-    """
-    computes mel-spectrograms from audio file and pulls the speaker ID from the
-    dataset
-    """
-
-    def __init__(self, dataset, format='speaker', speaker_utterances=4, mel_length = 128, mel_type = 'Tacotron'):
-        self.dataset = dataset
-        self.set_format(format)
-        self.speaker_utterances = speaker_utterances
-        self.mel_length = mel_length
-        self.mel_type = mel_type
-        self.mel_generators = dict()
-
-    def set_format(self,format):
-        self.format = format
-
-        if format == 'speaker':
-            self.create_speaker_index()
-
-    def create_speaker_index(self):
-        vals = [x.split('-',1) for x in self.dataset._walker]
-        speaker_map = defaultdict(list)
-
-        for i,v in enumerate(vals):
-            speaker_map[v[0]].append(i)
-        
-        self.speaker_map = speaker_map
-        self.speaker_keys = list(speaker_map.keys())
-
-    def apply_mel_gen(self, waveform, sampling_rate, channels=80):
-        if (sampling_rate, channels) not in self.mel_generators:
-            if self.mel_type == 'MFCC':
-                mel_gen = transforms.MFCC(sample_rate=sampling_rate, n_mfcc=channels)
-            elif self.mel_type == 'Mel':
-                mel_gen = transforms.MelSpectrogram(sample_rate=sampling_rate, n_mels=channels)
-            elif self.mel_type == 'Tacotron':
-                mel_gen = layers.TacotronSTFT(sampling_rate=sampling_rate,n_mel_channels=channels)
-            else:
-                raise NotImplementedError('Unsupported mel_type in MelSpeakerLoader: '+self.mel_type)
-            self.mel_generators[(sampling_rate,channels)] = mel_gen
-        else:
-            mel_gen = self.mel_generators[(sampling_rate, channels)]
-
-        if self.mel_type == 'Tacotron':
-            #Replicating from Tacotron2 data loader 
-            max_wav_value=32768.0
-            #skip normalization from Tacotron2, LibriSpeech data looks pre-normalized (all vals between 0-1)
-            audio_norm = waveform #/ max_wav_value
-            audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
-            melspec = mel_gen.mel_spectrogram(audio_norm)
-        else:
-            audio = waveform.unsqueeze(0)
-            audio = torch.autograd.Variable(audio, requires_grad=False)
-            melspec = mel_gen(audio)
-        
-        return melspec
-
-    def get_mel(self, waveform, sampling_rate, channels=80):
-        # We previously identified that these warnings were ok.
-        with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', message=r'At least one mel filterbank has all zero values.*', module=r'torchaudio.*')
-            melspec = self.apply_mel_gen(waveform, sampling_rate, channels)
-            # melspec is (1,1,channels, time) by default
-            # return (time, channels)
-            melspec = torch.squeeze(melspec).T
-            return melspec
-
-    def __getitem__(self, index):
-        if self.format == 'utterance':
-            (waveform, sample_rate, _, speaker_id, _, _) = self.dataset[index]
-            mel = self.get_mel(waveform, sample_rate)
-            return (speaker_id, mel)
-        elif self.format == 'speaker':
-            speaker_id = self.speaker_keys[index]
-            utter_indexes = random.sample(self.speaker_map[speaker_id], self.speaker_utterances)
-            mels = []
-            for i in utter_indexes:
-                (waveform, sample_rate, _, speaker_id, _, _) = self.dataset[i]
-                mel = self.get_mel(waveform, sample_rate)
-                if mel.shape[0] < self.mel_length:
-                    #Zero pad mel on the right to mel_length
-                    #pad_tuple is (dn start, dn end, dn-1 start, dn-1 end, ... , d1 start, d1 end)
-                    pad_tuple = (0,0,0,self.mel_length-mel.shape[0])
-                    mel=torch.nn.functional.pad(mel,pad_tuple)
-                    mel_frame = 0
-                else:
-                    mel_frame = random.randint(0,mel.shape[0]-self.mel_length)
-                mels.append(mel[mel_frame:mel_frame+self.mel_length,:])
-            return (speaker_id, torch.stack(mels,0))
-        else:
-            raise NotImplementedError()
-
-    def __len__(self):
-        if self.format == 'utterance':
-            return len(self.dataset)
-        elif self.format == 'speaker':
-            return len(self.speaker_keys)
-        else:
-            raise NotImplementedError()
\ No newline at end of file
diff --git a/speaker/model.py b/speaker/model.py
deleted file mode 100644
index 40de26d4c84c753075f88891edbcbc53daf41e1f..0000000000000000000000000000000000000000
--- a/speaker/model.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from torch import nn
-import numpy as np
-import torch
-from torch.nn.utils import clip_grad_norm_
-
-class SpeakerEncoder(nn.Module):
-    """ Learn speaker representation from speech utterance of arbitrary lengths.
-    """
-    def __init__(self, device, loss_device):
-        super().__init__()
-        self.loss_device = loss_device
-
-        # lstm block consisting of 3 layers
-        # takes input 80 channel log-mel spectrograms, projected to 256 dimensions
-        self.lstm = nn.LSTM(
-            input_size=80,
-            hidden_size=256,
-            num_layers=3,
-            batch_first=True,
-            dropout=0,
-            bidirectional=False
-        ).to(device)
-
-        self.linear = nn.Linear(in_features=256, out_features=256).to(device)
-        self.relu = nn.ReLU().to(device)
-        # epsilon term for numerical stability ( ie - division by 0)
-        self.epsilon = 1e-5
-
-        #Cosine similarity weights
-        self.sim_weight = nn.Parameter(torch.tensor([5.])).to(loss_device)
-        self.sim_bias = nn.Parameter(torch.tensor([-1.])).to(loss_device)
-
-    def forward(self, utterances, h_init=None, c_init=None):
-        # implement section 2.1 from https://arxiv.org/pdf/1806.04558.pdf
-        if h_init is None or c_init is None:
-            out, (hidden, cell) = self.lstm(utterances)
-        else:
-            out, (hidden, cell) = self.lstm(utterances, (h_init, c_init))
-
-        # compute speaker embedding from hidden state of final layer
-        final_hidden = hidden[-1]
-        speaker_embedding = self.relu(self.linear(final_hidden))
-
-        # l2 norm of speaker embedding
-        speaker_embedding = speaker_embedding / (torch.norm(speaker_embedding, dim=1, keepdim=True) + self.epsilon)
-        return speaker_embedding
-
-    def gradient_clipping(self):
-        self.sim_weight.grad *= 0.01
-        self.sim_bias.grad *= 0.01
-
-        #Pytorch to clip gradients if norm greater than max
-        clip_grad_norm_(self.parameters(),max_norm=3,norm_type=2)
-
-    def similarity_matrix(self, embeds, debug=False):
-        # calculate s_ji,k from section 2.1 of GE2E paper
-        # output matrix is cosine similarity between each utterance x centroid of each speaker
-        # embeds input size: (speakers, utterances, embedding size)
-
-        # Speaker centroids
-        # Equal to average of utterance embeddings for the speaker
-        # Used for neg examples (utterance comparing to false speaker)
-        # Equation 1 in paper
-        # size: (speakers, 1, embedding size)
-        speaker_centroid = torch.mean(embeds,dim=1,keepdim=True)
-
-        # Utterance exclusive centroids
-        # Equal to average of utterance embeddings for the speaker, excluding ith utterance
-        # Used for pos samples (utterance comparing to true speaker; speaker centroid exludes the utterance)
-        # Equation 8 in paper
-        # size: (speakers, utterances, embedding size)
-        num_utterance = embeds.shape[1]
-        utter_ex_centroid = (torch.sum(embeds,dim=1,keepdim=True) - embeds) / (num_utterance-1)
-
-        if debug:
-            print("e",embeds.shape)
-            print(embeds)
-            print("sc",speaker_centroid.shape)
-            print(speaker_centroid)
-            print("uc",utter_ex_centroid.shape)
-            print(utter_ex_centroid)
-
-        # Create pos and neg masks
-        num_speaker = embeds.shape[0]
-        i = torch.eye(num_speaker, dtype=torch.int)
-        pos_mask = torch.where(i)
-        neg_mask = torch.where(1-i)
-
-        if debug:
-            print("pm",len(pos_mask),len(pos_mask[0]))
-            print(pos_mask)
-            print("nm",len(neg_mask),len(neg_mask[0]))
-            print(neg_mask)
-
-        # Compile similarity matrix
-        # size: (speakers, utterances, speakers)
-        # initial size is (speakers, speakers, utterances for easier vectorization)
-        sim_matrix = torch.zeros(num_speaker, num_speaker, num_utterance).to(self.loss_device)
-        sim_matrix[pos_mask] = nn.functional.cosine_similarity(embeds,utter_ex_centroid,dim=2)
-        sim_matrix[neg_mask] = nn.functional.cosine_similarity(embeds[neg_mask[0]],speaker_centroid[neg_mask[1]],dim=2)
-        if debug:
-            print("sm",sim_matrix.shape)
-            print("pos vals",sim_matrix[pos_mask])
-            print("neg vals",sim_matrix[neg_mask])
-            print(sim_matrix)
-        
-        sim_matrix = sim_matrix.permute(0,2,1)
-
-        if debug:
-            print("sm",sim_matrix.shape)
-            print(sim_matrix)
-            print("cos sim weight", self.sim_weight)
-            print("cos sim bias", self.sim_bias)
-
-        # Apply weight / bias
-        sim_matrix = sim_matrix * self.sim_weight + self.sim_bias
-        return sim_matrix
-
-    def softmax_loss(self, embeds):
-        """
-        computes softmax loss as defined by equ 6 in the GE2E paper
-        :param embeds: shape (speakers, utterances, embedding size)
-        :return: computed softmax loss
-        """
-        # per the GE2E paper, softmax loss as defined by equ 6
-        # performs slightly better over Text-Independent Speaker
-        # Verification tasks.
-        # ref section 2.1 of the GE2E paper
-        speaker_count = embeds.shape[0]
-
-        # speaker, utterance, speaker
-        similarities = self.similarity_matrix(embeds)
-
-        # equ 6
-        loss_matrix = -similarities[torch.arange(0, speaker_count), :, torch.arange(0, speaker_count)] + \
-                      torch.log(torch.sum(torch.exp(similarities), dim=2))
-
-        # equ 10
-        return torch.sum(loss_matrix)
-
-    def contrast_loss(self, embeds):
-        """
-        computes contrast loss as defined by equ 7 in the GE2E paper
-        :param embeds: shape (speakers, utterances, embedding size)
-        :return: computed softmax loss
-        """
-        # per the GE2E paper, contrast loss as defined by equ 7
-        # performs slightly better over Text-Dependent Speaker
-        # Verification tasks.
-        # ref section 2.1 of the GE2E paper
-        speaker_count, utterance_count = embeds.shape[0:2]
-
-        # speaker, utterance, speaker
-        similarities = self.similarity_matrix(embeds)
-
-        # Janky indexing to resolve k != j
-        mask = torch.ones(similarities.shape, dtype=torch.bool)
-        mask[torch.arange(speaker_count), :, torch.arange(speaker_count)] = False
-        closest_neighbors, _ = torch.max(similarities[mask].reshape(speaker_count, utterance_count, speaker_count - 1), dim=2)
-
-        # Positive influence over matching embeddings
-        matching_embedding = similarities[torch.arange(0, speaker_count), :, torch.arange(0, speaker_count)]
-
-        # equ 7
-        loss_matrix = 1 - torch.sigmoid(matching_embedding) + torch.sigmoid(closest_neighbors)
-
-        # equ 10
-        return torch.sum(loss_matrix)
-
-    def accuracy(self, embeds):
-        """
-        computes argmax accuracy
-        :param embeds: shape (speakers, utterances, speakers)
-        :return: accuracy
-        """
-        num_speaker, num_utter = embeds.shape[:2]
-
-        similarities = self.similarity_matrix(embeds)
-        preds = torch.argmax(similarities, dim=2)
-        preds_one_hot = torch.nn.functional.one_hot(preds,num_classes = num_speaker)
-
-        actual = torch.arange(num_speaker).unsqueeze(1).repeat(1,num_utter)
-        actual_one_hot = torch.nn.functional.one_hot(actual,num_classes=num_speaker)
-
-        return torch.sum(preds_one_hot * actual_one_hot)/(num_speaker*num_utter)
-
-
-
-
-
-
diff --git a/speaker/preprocess.py b/speaker/preprocess.py
deleted file mode 100644
index e832afb3e40e7f17986f22858804b07de2070fc4..0000000000000000000000000000000000000000
--- a/speaker/preprocess.py
+++ /dev/null
@@ -1 +0,0 @@
-# Reference https://github.com/CorentinJ/Real-Time-Voice-Cloning/blob/0713f860a3dd41afb56e83cff84dbdf589d5e11a/encoder/preprocess.py#L16
\ No newline at end of file
diff --git a/speaker/saved_model.pt b/speaker/saved_model.pt
deleted file mode 100644
index 190dc8b2d33332fbcc4cdf137aed68cdb654f903..0000000000000000000000000000000000000000
--- a/speaker/saved_model.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6ccc0abcd0fb77104be73e6675454a06e7797bf1d4a1177181c32b648e9d75a9
-size 5697243
diff --git a/speaker/saved_model_e175.pt b/speaker/saved_model_e175.pt
deleted file mode 100644
index 8e736f6fda1845a64263d5077c4302af3f2a2c10..0000000000000000000000000000000000000000
--- a/speaker/saved_model_e175.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52ba80266b9f45fc3d825942aae40858eeaaa73994ba86e9ed017a533dc13323
-size 5861083
diff --git a/speaker/saved_models/dog.txt b/speaker/saved_models/dog.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/speaker/saved_models/saved_model_e175.pt b/speaker/saved_models/saved_model_e175.pt
deleted file mode 100644
index 8e736f6fda1845a64263d5077c4302af3f2a2c10..0000000000000000000000000000000000000000
--- a/speaker/saved_models/saved_model_e175.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:52ba80266b9f45fc3d825942aae40858eeaaa73994ba86e9ed017a533dc13323
-size 5861083
diff --git a/speaker/saved_models/saved_model_e273_LargeBatch.pt b/speaker/saved_models/saved_model_e273_LargeBatch.pt
deleted file mode 100644
index 11cadd34b6b7c8856596e28090f6f942a9973a7f..0000000000000000000000000000000000000000
--- a/speaker/saved_models/saved_model_e273_LargeBatch.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fbaaaa28a7d58b1316f322e1f33a5a68c00046b7b89a823ae7d987a632b8c7d6
-size 5861083
diff --git a/speaker/saved_models/saved_model_e300.pt b/speaker/saved_models/saved_model_e300.pt
deleted file mode 100644
index 8afe98c9e86b054ea8b521b56bf0920a0b55567c..0000000000000000000000000000000000000000
--- a/speaker/saved_models/saved_model_e300.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d9be127fb61b6d2306ff877ab2184f187450953a5555a6751b3616b5ed84e78a
-size 5698805
diff --git a/speaker/speakers.txt b/speaker/speakers.txt
deleted file mode 100644
index 6353474a4ab021393100cade62580b77207b0fab..0000000000000000000000000000000000000000
--- a/speaker/speakers.txt
+++ /dev/null
@@ -1,2497 +0,0 @@
-; Taken from the LibriSpeech data set...
-; Some pipe(|) separated metadata about all LibriVox readers, whose work was used
-; in the corpus.
-;
-; The meaning of the fields in left-to-right order is as follows:
-;
-; reader_id: the ID of the reader in the LibriVox's database
-; gender: 'F' for female, 'M' for male
-; subset: the corpus subset to which the reader's audio is assigned
-; duration: total number of minutes of speech by the reader, included in the corpus
-; name: the name under which the reader is registered in LibriVox
-;
-;ID  |SEX| SUBSET           |MINUTES| NAME
-14   | F | train-clean-360  | 25.03 | Kristin LeMoine
-16   | F | train-clean-360  | 25.11 | Alys AtteWater
-17   | M | train-clean-360  | 25.04 | Gord Mackenzie
-19   | F | train-clean-100  | 25.19 | Kara Shallenberg
-20   | F | train-other-500  | 30.07 | Gesine
-22   | F | train-clean-360  | 25.14 | Michelle Crandall
-23   | F | train-clean-360  | 25.23 | Anita Roy Dobbs
-25   | M | train-other-500  | 30.16 | John Gonzalez
-26   | M | train-clean-100  | 25.08 | Denny Sayers
-27   | M | train-clean-100  | 20.14 | Sean McKinley
-28   | F | train-clean-360  | 25.03 | Kristin Hughes
-29   | M | train-other-500  | 30.10 | Linton
-30   | F | train-clean-360  | 25.19 | Annie Coleman Rothenberg
-31   | M | train-other-500  | 23.79 | Martin Clifton
-32   | F | train-clean-100  | 24.01 | Betsie Bush
-36   | M | train-other-500  | 25.85 | Chip
-37   | M | train-other-500  | 30.04 | wolvrin
-38   | M | train-clean-360  | 25.11 | R. Francis Smith
-39   | F | train-clean-100  | 25.05 | Sherry Crowther
-40   | F | train-clean-100  | 25.04 | Vicki Barbour
-44   | F | train-other-500  | 25.90 | travelbratd
-45   | F | train-other-500  | 8.85  | Kelly Bescherer
-46   | M | train-other-500  | 30.02 | Aaron Hochwimmer
-47   | F | train-other-500  | 30.09 | Amanda
-49   | M | train-other-500  | 28.62 | vrk74
-51   | M | train-other-500  | 24.13 | rakkar
-52   | F | train-other-500  | 10.99 | Luisa Hall
-54   | F | train-clean-360  | 25.07 | Westwinds12
-55   | M | train-clean-360  | 25.21 | David Jaquay
-56   | F | train-clean-360  | 25.07 | Kirsten Ferreri
-57   | F | train-other-500  | 29.41 | Ophelia Darcy
-58   | M | train-other-500  | 30.06 | George Coutts
-60   | M | train-clean-100  | 20.18 | |CBW|Simon
-61   | M | test-clean       | 8.08  | Paul-Gabriel Wiener
-62   | M | train-other-500  | 17.80 | Faris
-64   | F | train-clean-360  | 23.51 | Robin Cotter
-65   | F | train-other-500  | 27.32 | chriss the girl
-66   | M | train-other-500  | 30.22 | Alex Foster
-70   | M | train-clean-360  | 21.61 | Kurt Copeland
-75   | M | train-other-500  | 30.10 | Jim Mowatt
-77   | F | train-other-500  | 30.10 | JemmaBlythe
-78   | M | train-clean-100  | 25.05 | Hugh McGuire
-79   | F | train-clean-360  | 25.01 | Jennifer Crispin
-81   | M | train-clean-360  | 25.22 | Aaron Decker
-82   | F | train-other-500  | 30.15 | Marlo Dianne
-83   | F | train-clean-100  | 25.04 | Catharine Eastman
-84   | F | dev-clean        | 8.02  | Christie Nowak
-85   | M | train-other-500  | 30.02 | David Leaman
-87   | F | train-clean-100  | 25.10 | Rosalind Wills
-89   | F | train-clean-100  | 25.08 | Kristen McQuillin
-90   | M | train-clean-360  | 25.17 | Dan Threetrees
-91   | M | train-other-500  | 30.05 | Stephan Mobius
-92   | F | train-other-500  | 26.78 | Cori Samuel
-93   | F | train-clean-360  | 25.02 | Kathy
-94   | M | train-other-500  | 30.11 | David Barnes
-98   | F | train-clean-360  | 25.03 | Patricia Oakley
-100  | F | train-clean-360  | 25.06 | Judy Bieber
-101  | M | train-clean-360  | 25.04 | paulino
-102  | F | train-other-500  | 30.18 | Linda Leu
-103  | F | train-clean-100  | 23.72 | Karen Savage
-104  | F | train-other-500  | 30.21 | Laura M.D.
-107  | M | train-other-500  | 18.64 | John Greenman
-110  | F | train-other-500  | 30.07 | Cynthia Lyons (1946-2011)
-111  | F | train-other-500  | 22.36 | Linda Wilcox
-112  | F | train-clean-360  | 25.23 | Christiane Levesque
-114  | F | train-clean-360  | 25.03 | Jen Kidd
-115  | F | train-clean-360  | 25.02 | Maddie
-116  | M | dev-other        | 10.02 | Steven Collins
-118  | M | train-clean-100  | 25.05 | Alex Buie
-119  | M | train-clean-360  | 25.11 | Alex Patterson
-121  | F | test-clean       | 8.01  | Nikolle Doolin
-122  | M | train-clean-360  | 25.15 | J.C.
-123  | F | train-other-500  | 30.01 | Ezwa
-125  | F | train-clean-100  | 25.18 | Claire Goget
-126  | F | train-clean-360  | 25.15 | Susan Denney
-127  | M | train-other-500  | 29.49 | John Hicken
-128  | M | train-other-500  | 25.63 | ML Cohen
-133  | M | train-other-500  | 13.89 | Mick
-147  | M | train-other-500  | 10.79 | Thomas Hoover
-149  | M | train-other-500  | 17.69 | Joshua Young
-150  | F | train-clean-100  | 25.13 | Fox in the Stars
-151  | F | train-other-500  | 10.54 | Gwen
-152  | M | train-other-500  | 28.67 | Andy Minter
-153  | M | train-other-500  | 30.16 | Graham Williams
-154  | M | train-clean-360  | 25.13 | Robert Foster
-157  | M | train-clean-360  | 22.96 | Ben Douglas
-159  | M | train-clean-360  | 25.03 | hugh mac
-161  | M | train-other-500  | 30.09 | Cyril Law, Jr.
-163  | M | train-clean-100  | 25.06 | Andrew Miller
-166  | F | train-clean-360  | 25.16 | Paula Berinstein
-167  | M | train-other-500  | 21.75 | Peter Yearsley
-168  | M | train-other-500  | 28.32 | Chris Goringe
-173  | F | train-other-500  | 11.25 | vlooi
-174  | M | dev-clean        | 8.04  | Peter Eastman
-175  | F | train-clean-360  | 25.03 | Meredith Hughes
-176  | M | train-clean-360  | 25.20 | Vinny Bove
-177  | F | train-other-500  | 26.69 | Kymm Zuckert
-188  | F | train-clean-360  | 25.26 | Mary Anderson
-192  | F | train-clean-360  | 25.19 | Deborah Clark
-196  | M | train-clean-100  | 25.24 | Stewart Wills
-198  | F | train-clean-100  | 25.02 | Heather Barnett
-199  | F | train-other-500  | 7.08  | Maria Morabe
-200  | F | train-clean-100  | 25.14 | Maureen S. O'Brien
-201  | M | train-clean-100  | 25.19 | Joplin James
-202  | F | train-other-500  | 30.04 | Geetu Melwani
-203  | F | train-clean-360  | 25.01 | Marian Brown
-204  | M | train-clean-360  | 25.18 | Mark F. Smith
-205  | F | train-clean-360  | 25.07 | Esther
-207  | M | train-clean-360  | 25.00 | Kevin McAsh
-208  | F | train-clean-360  | 13.28 | Andrea L
-209  | F | train-clean-360  | 25.08 | Moira Fogarty
-210  | M | train-clean-360  | 25.12 | Aldark
-211  | F | train-clean-100  | 25.22 | shanda_w
-215  | F | train-other-500  | 29.09 | Alice Elizabeth Still
-216  | M | train-clean-360  | 25.08 | Thomas Davoren
-217  | F | train-clean-360  | 17.71 | firefly
-218  | M | train-other-500  | 14.46 | Kelly Clear
-224  | F | train-clean-360  | 25.20 | Caitlin Kelly
-225  | F | train-clean-360  | 25.17 | Brenda Dayne
-226  | F | train-clean-100  | 25.21 | Deb Bacon-Ziegler
-227  | F | train-clean-360  | 25.16 | Sarah Key-DeLyria
-228  | F | train-other-500  | 28.84 | Arctura
-229  | M | train-clean-100  | 19.56 | carnright
-231  | M | train-clean-360  | 22.19 | Sean McGaughey
-233  | M | train-clean-100  | 24.72 | Steve Karafit
-237  | F | test-clean       | 8.02  | rachelellen
-238  | F | train-other-500  | 10.11 | Maria Elmvang
-240  | M | train-clean-360  | 25.06 | Shurtagal
-242  | F | train-clean-360  | 25.12 | Cagirlwithasoutherndrawl
-243  | F | train-other-500  | 30.04 | Jennifer Stearns
-245  | M | train-other-500  | 30.25 | Peter of Buckinghamshire England
-246  | F | train-clean-360  | 22.43 | Beth Dudek
-248  | F | train-clean-100  | 25.13 | Becky Miller
-249  | M | train-clean-360  | 18.69 | pww214
-250  | F | train-clean-100  | 25.16 | Mary Reagan
-251  | M | dev-clean        | 8.04  | Mark Nelson
-252  | M | train-other-500  | 30.26 | Rainer
-253  | M | train-other-500  | 27.43 | Stefan Schmelz
-254  | M | train-clean-100  | 25.06 | Alan Davis Drake (1945-2010)
-255  | M | train-other-500  | 30.10 | Chris Hawk
-258  | M | train-clean-360  | 24.53 | Kurt Wong
-260  | M | test-clean       | 8.05  | Brad Bush
-263  | M | train-other-500  | 16.03 | Michael Sirois
-264  | M | train-other-500  | 22.55 | Chris Hughes
-265  | M | train-other-500  | 10.38 | Janice
-272  | M | train-clean-360  | 16.45 | Mr. Baby Man
-273  | M | train-other-500  | 30.02 | Jim Cadwell
-274  | F | train-clean-360  | 25.18 | Eileen George
-277  | F | train-other-500  | 25.72 | Alessia
-278  | F | train-clean-360  | 22.05 | AliceG
-283  | M | train-other-500  | 15.42 | Asaf Bartov
-288  | F | train-clean-360  | 25.13 | Bookworm
-289  | F | train-clean-100  | 19.64 | Barbara Wedge
-294  | F | train-other-500  | 20.51 | Calliope
-296  | M | train-clean-360  | 25.16 | Carl Manchester
-298  | F | train-clean-100  | 25.00 | Caroline Morse
-302  | F | train-clean-100  | 25.04 | Chris Peterson
-303  | M | train-clean-360  | 25.03 | Tony Hightower
-307  | M | train-clean-100  | 25.11 | Randy Phillips
-310  | F | train-other-500  | 25.15 | Dexnell Peters
-311  | M | train-clean-100  | 25.17 | deadwhitemales
-313  | F | train-other-500  | 30.03 | Dilini Jayasinghe
-317  | M | train-other-500  | 28.14 | Mike Gardom
-318  | F | train-clean-360  | 25.17 | Eileen aka e
-319  | M | train-other-500  | 30.07 | Ed Good
-322  | F | train-clean-100  | 25.20 | Elisabeth Shields
-323  | F | train-clean-360  | 14.36 | Erica Kuntz
-328  | F | train-clean-100  | 19.35 | Elizabeth Palmer
-329  | M | train-clean-360  | 25.09 | Todd Cranston-Cuebas
-331  | M | train-other-500  | 30.12 | Richard Grove
-332  | M | train-clean-100  | 19.00 | Aaron Teiser
-335  | M | train-clean-360  | 25.08 | Paul Harvey
-336  | M | train-other-500  | 21.97 | Harvey Chinn
-337  | F | train-clean-360  | 25.15 | Barbara Harvey
-339  | F | train-clean-360  | 25.07 | Heather Ordover
-340  | M | train-clean-360  | 25.17 | Scott Henkel
-345  | M | train-clean-360  | 25.19 | Micah Sheppard
-348  | M | train-other-500  | 30.02 | ianish
-353  | M | train-clean-360  | 20.10 | Jamey Osborne
-359  | M | train-clean-360  | 25.08 | John Nicholson
-362  | F | train-clean-360  | 25.07 | Judith Brown
-365  | M | train-other-500  | 30.01 | Jon Ingram
-366  | F | train-other-500  | 30.12 | Katy Preston
-367  | F | test-other       | 6.74  | Kathleen Dang
-369  | M | train-clean-360  | 19.56 | Kevin Readdean
-373  | F | train-clean-360  | 25.18 | Kim Braun
-374  | M | train-clean-100  | 25.16 | kumarei
-377  | M | train-other-500  | 21.39 | Lenny Glionna Jr.
-380  | F | train-clean-360  | 25.15 | Laurie Campbell
-392  | F | train-other-500  | 23.64 | Maria Celano
-398  | M | train-clean-360  | 23.56 | James Smith
-402  | F | train-other-500  | 30.19 | Sharmini Kumar
-403  | F | train-clean-100  | 25.14 | Nocturna
-404  | F | train-other-500  | 24.68 | Nomenphile
-405  | M | train-clean-100  | 25.04 | Eric Dennison
-408  | F | train-clean-360  | 25.17 | Claudine Chen
-409  | M | train-clean-360  | 25.13 | Mike Kauffmann
-412  | M | train-clean-100  | 22.38 | Brian Roberg
-413  | M | train-other-500  | 19.72 | Daniel Watkins
-421  | M | train-other-500  | 29.09 | Patrick
-422  | M | dev-clean        | 8.38  | President Lethe
-426  | F | train-clean-100  | 25.21 | Norah Piehl
-428  | M | train-other-500  | 27.22 | Rayburn Beale
-432  | M | train-other-500  | 30.10 | Steve Andersen
-434  | F | train-clean-360  | 25.09 | Joyce Nussbaum
-439  | M | train-clean-360  | 25.16 | Robert Garrison
-441  | F | train-clean-100  | 25.19 | Sandra in Wales, United Kingdom
-444  | F | train-other-500  | 28.32 | Sage Tyrtle
-445  | M | train-clean-100  | 11.08 | Dave Foss
-446  | M | train-clean-100  | 25.00 | Steve Hartzog
-448  | F | train-other-500  | 23.84 | scrappylibrarian
-451  | F | train-clean-360  | 22.93 | Sonserae Leese-Calver
-453  | M | train-other-500  | 22.78 | Glen Hallstrom
-454  | M | train-clean-360  | 25.20 | Tom Yates
-458  | M | train-clean-100  | 17.30 | Scott Splavec
-459  | M | train-clean-360  | 25.05 | Mark Bradford
-460  | M | train-clean-100  | 25.23 | Dave Ranson
-464  | M | train-clean-360  | 18.24 | Mike Wilson
-466  | F | train-other-500  | 30.21 | Joy Chan
-470  | M | train-other-500  | 30.06 | Chris Chapman
-472  | F | train-clean-360  | 25.02 | Tina Tilney
-474  | M | train-other-500  | 29.00 | Zachary Brewster-Geisz
-475  | M | train-clean-360  | 23.95 | Jason X.
-476  | M | train-clean-360  | 25.08 | Chuck Spann
-479  | F | train-clean-360  | 25.00 | wedschild
-480  | M | train-clean-360  | 25.13 | Chris Vee
-481  | M | train-clean-100  | 25.08 | Neal Foley
-483  | F | train-other-500  | 26.75 | junk science
-487  | M | train-clean-360  | 17.12 | Clayton J. Smith
-489  | F | train-other-500  | 26.53 | Tora
-492  | M | train-clean-360  | 25.19 | TBOL3
-497  | M | train-clean-360  | 25.00 | audiotoshokan
-500  | M | train-clean-360  | 25.04 | galaxiant
-501  | M | train-clean-360  | 25.06 | mikenkat
-505  | M | train-other-500  | 29.49 | Menno
-510  | M | train-clean-360  | 25.07 | Kirk Thomas
-511  | M | train-clean-360  | 25.23 | Matthew Shepherd
-512  | M | train-clean-360  | 25.12 | Anthony Craine
-517  | M | train-other-500  | 23.59 | Matthew Walton
-525  | F | train-clean-360  | 25.03 | Victoria Long
-533  | F | test-other       | 10.05 | Ana Simao
-534  | F | train-clean-360  | 25.01 | Jean O'Sullivan
-542  | M | train-other-500  | 29.87 | J. Hall
-543  | M | train-clean-360  | 25.10 | Ted Delorme
-544  | M | train-other-500  | 25.89 | bozgeez
-548  | M | train-clean-360  | 25.14 | Chris Mitchell
-549  | F | train-clean-360  | 18.99 | SarahHadley
-551  | M | train-other-500  | 30.20 | Guntar
-557  | M | train-other-500  | 30.07 | fieldsofgold
-559  | M | train-clean-360  | 25.16 | Bill Stackpole
-561  | M | train-clean-360  | 24.78 | Quentin
-567  | M | train-other-500  | 6.49  | Aaron Benedict
-568  | M | train-other-500  | 30.21 | JD Weber
-569  | M | train-other-500  | 8.36  | Frank
-572  | M | train-other-500  | 29.18 | Rebecca Dittman
-576  | F | train-clean-360  | 20.69 | Caroline Mercier
-580  | M | train-clean-360  | 25.22 | Ryan
-581  | M | train-clean-360  | 25.20 | C. Berrius
-583  | M | train-clean-360  | 24.67 | Russ Maxwell
-584  | F | train-other-500  | 30.10 | miette
-585  | F | train-other-500  | 30.05 | pheo
-587  | F | train-clean-100  | 25.20 | Joy Scaglione
-589  | F | train-clean-360  | 25.01 | Stephanie Konig
-593  | M | train-clean-360  | 24.55 | Eric S. Piotrowski
-594  | M | train-clean-360  | 25.12 | KentF
-596  | F | train-clean-360  | 25.00 | Carol Goode
-597  | F | train-clean-360  | 25.25 | Lisa Chau
-598  | F | train-clean-360  | 25.02 | Kim
-606  | M | train-clean-360  | 25.02 | Julian Jamison
-608  | F | train-other-500  | 30.09 | Baranduin
-612  | F | train-clean-360  | 21.70 | Cindy Steib
-613  | M | train-other-500  | 29.85 | D.E. Wittkower
-614  | F | train-other-500  | 27.57 | Christine Blachford
-622  | M | train-other-500  | 23.15 | Andrew Lebrun
-625  | M | train-clean-100  | 25.01 | toriasuncle
-636  | F | train-clean-360  | 25.03 | Zale Schafer (Rose May Chamberlin Memorial Foundat
-637  | M | train-clean-360  | 25.11 | Michael Scherer
-639  | M | train-clean-360  | 25.10 | Robert Beach
-644  | M | train-other-500  | 30.17 | Daniele
-652  | M | dev-clean        | 8.31  | Scott Walter
-663  | M | train-clean-360  | 25.10 | Bruce Stafford
-664  | F | train-clean-360  | 25.20 | Wendy G.
-666  | F | train-clean-360  | 24.58 | Monique
-667  | F | train-clean-360  | 25.04 | Bethany Simpson
-669  | F | train-clean-100  | 25.17 | Anne
-671  | M | train-clean-360  | 24.11 | koijmonop
-672  | M | test-clean       | 8.27  | Taylor Burton-Edward
-679  | M | train-other-500  | 13.04 | rhodian
-681  | F | train-other-500  | 30.01 | Lucy Burgoyne
-684  | F | train-other-500  | 30.04 | Lizzie Driver
-688  | F | train-clean-360  | 25.19 | J. M. Smallheer
-690  | F | train-other-500  | 22.10 | Silver
-696  | F | train-clean-100  | 25.11 | Tamara R. Schwartz
-698  | F | train-clean-360  | 25.03 | Randi Warwick
-699  | F | train-clean-360  | 25.06 | Diana Kiesners
-700  | F | dev-other        | 10.01 | Susan Hooks
-705  | F | train-other-500  | 30.09 | eva
-707  | M | train-clean-360  | 24.76 | Jason Mayoff
-708  | M | train-clean-360  | 21.01 | Kevin Devine
-711  | M | train-clean-360  | 25.13 | Roy Schreiber
-712  | M | train-other-500  | 28.91 | Michael Shook
-713  | F | train-other-500  | 25.71 | Onjana Yawnghwe
-716  | M | train-clean-360  | 22.52 | martyd
-718  | M | train-clean-360  | 25.08 | clarknova
-720  | M | train-other-500  | 30.09 | Peter Gallagher
-724  | M | train-clean-360  | 25.08 | Michael Crowl
-726  | M | train-other-500  | 30.19 | Paul
-727  | M | train-other-500  | 27.18 | Andrew Richards
-728  | M | train-other-500  | 30.21 | Eric Connover
-730  | F | train-clean-100  | 25.19 | Karen Labenz
-731  | F | train-clean-360  | 25.14 | Megan-Jane Daniels Suyasu
-737  | M | train-other-500  | 24.83 | Roger W. Barnett
-742  | M | train-other-500  | 18.38 | Peter Groom
-753  | M | train-other-500  | 26.92 | Tim Bulkeley
-764  | M | train-clean-360  | 25.08 | Carl Vonnoh, III
-766  | M | train-other-500  | 24.41 | Jean Crevier
-770  | M | train-clean-360  | 25.00 | Justin S Barrett
-774  | F | train-other-500  | 8.75  | Sara Walsh
-777  | M | dev-clean        | 8.06  | fling93
-778  | M | train-other-500  | 30.17 | Branko Collin
-779  | M | train-other-500  | 30.09 | Greg
-780  | M | train-other-500  | 24.65 | Lloyd Davis
-781  | M | train-clean-360  | 25.12 | Mitchell Dwyer
-782  | F | train-other-500  | 30.18 | Michele Pacey
-783  | F | train-clean-360  | 23.98 | Gina
-789  | F | train-other-500  | 17.71 | Eva
-791  | M | train-other-500  | 27.56 | Ivan
-792  | F | train-other-500  | 19.73 | mjd-s
-797  | M | train-other-500  | 13.94 | Mike Shapiro
-803  | M | train-clean-360  | 25.02 | Greg Elmensdorp
-806  | M | train-clean-360  | 22.41 | Aaron Andradne
-807  | M | train-other-500  | 25.31 | Luke Venediger
-810  | M | train-other-500  | 21.54 | Joseph Loverti
-811  | F | train-other-500  | 25.09 | Elizabeth
-815  | M | train-clean-360  | 16.20 | mawrtea
-816  | M | train-clean-360  | 23.23 | Jeff Robinson
-820  | M | train-clean-360  | 24.64 | Scoot
-826  | M | train-other-500  | 18.49 | Thomas Wells
-829  | F | train-clean-360  | 25.14 | frenchfry
-830  | F | train-clean-360  | 22.26 | sebrazer
-831  | M | train-clean-100  | 25.22 | Nick Gallant
-834  | F | train-clean-360  | 25.14 | nausicaa
-835  | M | train-clean-360  | 18.61 | echo
-836  | M | train-clean-360  | 25.01 | Kevin LaVergne
-839  | M | train-clean-100  | 19.95 | rovert405
-844  | F | train-other-500  | 27.30 | Martina
-845  | M | train-other-500  | 11.71 | DaveF
-846  | M | train-other-500  | 30.12 | Anadaxis_Canejia
-850  | M | train-clean-360  | 22.03 | tonypettit
-851  | M | train-other-500  | 9.02  | brenthumphries
-868  | M | train-clean-360  | 25.07 | Mike Rosenlof
-876  | F | train-other-500  | 22.85 | Alisha
-882  | F | train-clean-360  | 24.14 | Mur Lafferty
-884  | M | train-other-500  | 17.23 | sayeth
-886  | M | train-other-500  | 24.31 | Paul S. Jenkins
-887  | F | train-clean-100  | 25.11 | Lana Taylor
-895  | M | train-other-500  | 15.01 | Max Porter Zasada
-899  | M | train-clean-360  | 25.09 | thomahal
-908  | M | test-clean       | 8.05  | Sam Stinson
-909  | M | train-clean-100  | 25.00 | Greg Bryant
-911  | M | train-clean-100  | 23.83 | frankjf
-915  | M | train-other-500  | 21.21 | Ted Kaouk
-920  | F | train-clean-360  | 21.36 | Sarah Bean
-921  | M | train-other-500  | 29.10 | Brian J. Callaghan
-922  | M | train-clean-360  | 25.07 | Steven H. Wilson
-923  | F | train-other-500  | 21.33 | Layna
-925  | F | train-clean-360  | 25.22 | pattymarie
-927  | M | train-other-500  | 26.13 | Nerijus
-937  | F | train-other-500  | 30.14 | Susie G.
-948  | F | train-clean-360  | 25.13 | Chere Theriot
-949  | M | train-clean-360  | 25.08 | ontheroad
-951  | F | train-other-500  | 30.04 | thomasina
-953  | M | train-clean-360  | 20.76 | Jim Mullins
-954  | M | train-clean-360  | 14.44 | Brooks Seveer
-956  | F | train-other-500  | 23.96 | krithiga
-957  | M | train-clean-360  | 25.06 | iscatel
-960  | F | train-other-500  | 27.55 | Marloes Schoonheim
-964  | M | train-other-500  | 17.09 | Paul Sze
-968  | F | train-clean-360  | 24.49 | Pat Elder
-969  | M | train-other-500  | 30.10 | Czechchris
-976  | F | train-other-500  | 30.11 | Alison Raouf
-978  | M | train-other-500  | 27.56 | Rick Box
-979  | F | train-clean-360  | 5.59  | Kelli Robinson
-982  | M | train-other-500  | 22.28 | Mike Roop
-984  | M | train-clean-360  | 23.39 | J A Carter
-985  | M | train-other-500  | 30.05 | George Pilling
-986  | M | train-clean-360  | 15.96 | Michael Kirkpatrick
-1001 | M | train-clean-360  | 25.25 | Eric
-1006 | F | train-other-500  | 30.00 | Marta Kornowska
-1012 | F | train-clean-360  | 21.38 | Lizzie Oldfather
-1018 | M | train-clean-360  | 25.07 | JimmyLogan
-1025 | M | train-clean-360  | 22.99 | rdmagpie
-1027 | M | train-clean-360  | 25.09 | Brooks Jensen
-1028 | M | train-clean-360  | 25.03 | Tim Lundeen
-1031 | F | train-clean-360  | 23.73 | swroot
-1034 | M | train-clean-100  | 16.71 | Kevin O'Coin
-1040 | M | train-clean-100  | 15.70 | John Garvin
-1046 | M | train-clean-360  | 24.65 | durnburr
-1049 | M | train-other-500  | 14.93 | Sam Fold
-1050 | F | train-clean-360  | 25.04 | entada
-1051 | F | train-other-500  | 28.33 | E. Moulton
-1052 | F | train-clean-360  | 25.13 | Kathy Jacobs
-1053 | F | train-clean-360  | 25.04 | katyleah
-1054 | F | train-clean-360  | 24.80 | Igor Teaforay
-1058 | M | train-clean-360  | 25.10 | James Tiley
-1060 | F | train-clean-360  | 20.07 | Val Grimm
-1061 | F | train-clean-360  | 25.14 | Missie
-1065 | M | train-other-500  | 30.18 | Justin Brett
-1066 | F | train-clean-360  | 25.20 | Laurie Anne Walden
-1069 | F | train-clean-100  | 25.15 | Dawn
-1079 | F | train-clean-360  | 21.26 | Mary aka Breadchick
-1081 | M | train-clean-100  | 25.18 | Fracture
-1084 | F | train-other-500  | 29.12 | Nichole Karl
-1085 | M | train-other-500  | 30.24 | hefyd
-1088 | F | train-clean-100  | 25.18 | Christabel
-1089 | M | test-clean       | 8.05  | Peter Bobbe
-1092 | F | train-other-500  | 30.17 | Maria
-1093 | F | train-clean-360  | 25.11 | Kiki Baessell
-1094 | M | train-other-500  | 30.03 | tubeyes
-1096 | M | train-other-500  | 26.95 | Geoff Dugwyler
-1097 | M | train-other-500  | 30.18 | Euthymius
-1098 | F | train-clean-100  | 18.31 | Merryb
-1100 | F | train-clean-360  | 23.87 | Danielle Flores
-1107 | M | train-other-500  | 30.10 | Jason Oakley
-1110 | M | train-other-500  | 24.85 | Graeme Jolliffe
-1112 | M | train-clean-360  | 25.15 | RedToby
-1116 | F | train-clean-100  | 25.12 | Megan Stemm-Wade
-1121 | M | train-clean-360  | 25.12 | John Lieder
-1124 | F | train-other-500  | 30.03 | Ancilla
-1132 | M | train-other-500  | 30.04 | Giles Baker
-1152 | F | train-other-500  | 28.04 | Millbeach
-1154 | F | train-other-500  | 30.12 | Larysa Jaworski
-1160 | M | train-clean-360  | 25.20 | Gary Gilberd
-1161 | M | train-other-500  | 21.87 | Dominic Moore
-1165 | M | train-clean-360  | 25.13 | Bob Graff
-1166 | F | train-other-500  | 21.27 | Debra Lynn
-1168 | F | train-other-500  | 21.61 | Ree
-1171 | F | train-other-500  | 30.01 | Julia Claussen
-1175 | M | train-clean-360  | 25.02 | Brother Patrick
-1179 | M | train-other-500  | 28.84 | Alan Chant
-1182 | M | train-clean-360  | 25.06 | Brett Condron
-1183 | F | train-clean-100  | 9.72  | roolynninms
-1184 | M | train-other-500  | 28.84 | Jeremy Pavier
-1187 | M | train-other-500  | 30.02 | Paul Hansen
-1188 | M | test-clean       | 8.20  | Duncan Murrell
-1195 | F | train-clean-360  | 25.03 | Jennette Selig
-1200 | M | train-other-500  | 30.04 | hosmer_angel
-1212 | F | train-clean-360  | 25.07 | Lee Ann Howlett
-1221 | F | test-clean       | 8.07  | Dianne
-1222 | M | train-clean-360  | 25.12 | Joseph Ugoretz
-1224 | F | train-clean-360  | 22.11 | Heather Duncan
-1225 | M | train-other-500  | 30.08 | Chris Langston
-1226 | M | train-clean-360  | 25.11 | Russ Lemker
-1230 | M | train-other-500  | 30.03 | Ian Skillen
-1235 | M | train-clean-100  | 25.09 | Tim Gregory
-1239 | M | train-other-500  | 20.69 | Scott Robbins
-1241 | F | train-clean-360  | 25.12 | Catherine Fitz
-1246 | F | train-clean-100  | 25.09 | Sandra
-1250 | F | train-other-500  | 30.23 | Christina Boyles
-1252 | F | train-other-500  | 15.47 | Avery
-1255 | M | dev-other        | 10.03 | Simon Evers
-1258 | M | train-other-500  | 18.03 | Mellors
-1259 | F | train-clean-360  | 25.12 | Elizabeth Klett
-1260 | M | train-other-500  | 24.01 | Chris Hughes
-1261 | M | train-other-500  | 21.04 | Mans Broo
-1263 | F | train-clean-100  | 25.16 | Leonie Rose
-1264 | M | train-clean-360  | 25.01 | Matthew Hinman
-1265 | M | train-clean-360  | 25.18 | Edward Elmer
-1266 | F | train-other-500  | 27.94 | Jenilee
-1271 | M | train-clean-360  | 25.18 | Christian Pecaut
-1272 | M | dev-clean        | 8.02  | John Rose
-1274 | M | train-other-500  | 15.99 | Larry Gilman
-1280 | M | train-other-500  | 23.94 | Tim Makarios
-1283 | M | train-clean-360  | 24.22 | Paul Siegel
-1284 | F | test-clean       | 8.16  | Daniel Anaya
-1289 | F | train-clean-360  | 25.00 | Joanne Pauwels
-1290 | F | train-clean-360  | 15.49 | librarianite
-1291 | F | train-other-500  | 28.53 | Patti Brugman
-1296 | F | train-clean-360  | 25.09 | Gigi Minden
-1298 | F | train-other-500  | 30.03 | Wina Hathaway
-1311 | M | train-clean-360  | 25.04 | Scott D. Farquhar
-1313 | M | train-clean-360  | 25.18 | Scott Sherris
-1316 | M | train-clean-360  | 25.04 | Estragon
-1320 | M | test-clean       | 8.02  | number6
-1322 | M | train-clean-360  | 25.17 | chris tierney
-1323 | M | train-clean-360  | 25.08 | Leon Mire
-1331 | M | train-other-500  | 28.73 | Adrian Praetzellis
-1334 | M | train-clean-100  | 23.89 | John Schell
-1335 | F | train-clean-360  | 25.08 | Clarica
-1336 | M | train-clean-360  | 25.19 | Charlie Blakemore
-1337 | M | train-clean-360  | 25.02 | Steven Rushing
-1341 | M | train-other-500  | 28.77 | Coastalbloke
-1342 | F | train-other-500  | 30.20 | SueAnn Dozier
-1343 | F | train-clean-360  | 25.13 | Laura Koskinen
-1347 | M | train-other-500  | 21.35 | Ted Nugent
-1348 | F | train-clean-360  | 22.68 | Janet Friday
-1349 | M | train-clean-360  | 25.14 | John Pruden
-1353 | M | train-other-500  | 30.04 | Clarke Bell
-1355 | M | train-clean-100  | 22.31 | Chris Gladis
-1363 | F | train-clean-100  | 21.87 | Tammy Sanders
-1365 | M | train-clean-360  | 25.19 | Joel Poortenga
-1367 | M | train-other-500  | 30.12 | Joe Brenneman
-1370 | F | train-other-500  | 19.78 | Lee Elliott
-1373 | F | train-other-500  | 26.93 | Kira Belkin
-1374 | M | train-other-500  | 17.76 | Graham Thomsen
-1379 | M | train-clean-360  | 25.09 | Ken Crooker
-1382 | F | train-clean-360  | 21.44 | Heather Lawrence
-1383 | M | train-clean-360  | 23.92 | David Best
-1384 | M | train-other-500  | 24.51 | Stephen Lamb
-1387 | M | train-clean-360  | 25.05 | Scott Mather
-1390 | F | train-clean-360  | 16.65 | C.L.Coney
-1392 | M | train-clean-360  | 25.22 | Chris Masterson
-1401 | F | train-clean-360  | 25.19 | Sibella Denton
-1403 | M | train-other-500  | 17.40 | tipaew
-1413 | M | train-clean-360  | 25.08 | ryanaw
-1414 | F | train-other-500  | 7.83  | guava
-1417 | F | train-clean-360  | 25.19 | Psuke Bariah
-1421 | F | train-other-500  | 28.68 | Madame Tusk
-1422 | F | train-clean-360  | 25.19 | Yazpistachio
-1425 | F | train-clean-360  | 25.26 | Jeanette Ferguson
-1430 | M | train-other-500  | 23.75 | Alok Karulkar
-1444 | M | train-other-500  | 11.74 | Ryan Mease
-1445 | M | train-clean-360  | 25.15 | Michael Yard
-1446 | M | train-clean-360  | 25.01 | Michael Loftus
-1447 | F | train-clean-100  | 25.18 | Luigina
-1448 | F | train-clean-360  | 25.07 | marevalo
-1455 | M | train-clean-100  | 25.13 | webslog
-1456 | M | train-clean-360  | 25.12 | Jason Isbell
-1460 | F | train-clean-360  | 25.01 | E. Plein
-1462 | F | dev-clean        | 8.04  | E. Tavano
-1463 | F | train-clean-360  | 25.23 | Vivian Bush
-1469 | M | train-other-500  | 16.96 | Fr. Richard Zeile of Detroit
-1472 | F | train-clean-360  | 25.15 | Sarah Jennings
-1473 | M | train-clean-360  | 25.10 | Dan Polanco
-1474 | F | train-other-500  | 30.03 | Jc Guan
-1482 | M | train-clean-360  | 25.00 | Joshua B. Christensen
-1485 | M | train-other-500  | 24.24 | Robert Flach
-1487 | M | train-clean-360  | 23.93 | radioreader
-1492 | M | train-other-500  | 30.14 | mb
-1494 | M | train-other-500  | 6.27  | George Deprez, PhD
-1495 | M | train-other-500  | 24.44 | Glendower Jones
-1498 | F | train-clean-360  | 18.38 | Lori Hebel
-1502 | F | train-clean-100  | 25.05 | Ann Boyer
-1505 | M | train-other-500  | 25.98 | Mark Norman
-1509 | F | train-clean-360  | 25.04 | Miranda Stinson
-1513 | M | train-clean-360  | 25.11 | Simon-Peter Zak
-1535 | M | train-clean-360  | 22.74 | Robert Scott
-1536 | M | train-clean-360  | 25.23 | Marco
-1544 | F | train-other-500  | 30.18 | LilianaVale
-1545 | F | train-other-500  | 9.17  | AmyAG
-1547 | F | train-clean-360  | 17.45 | Riddleman
-1552 | M | train-clean-360  | 15.62 | Roger Turnau
-1553 | F | train-clean-100  | 22.49 | Mim Ritty
-1556 | M | train-clean-360  | 25.13 | geofred
-1559 | M | train-other-500  | 18.95 | Luke Harrison
-1563 | F | train-other-500  | 13.26 | Chandra Gioiello
-1564 | F | train-other-500  | 22.58 | Hedvig
-1566 | F | train-other-500  | 27.46 | Anna Christensen
-1569 | F | train-other-500  | 25.00 | Kristine Mackin
-1571 | M | train-clean-360  | 25.24 | Bob Tassinari
-1572 | M | train-other-500  | 30.14 | Alan Clare
-1578 | F | train-clean-100  | 25.01 | Lorelle Anderson
-1579 | F | train-other-500  | 30.06 | Philippa Willitts
-1580 | F | test-clean       | 8.07  | TinyPines
-1585 | F | dev-other        | 10.03 | Nelly ()
-1593 | F | train-other-500  | 28.90 | Kristine Bekere
-1594 | M | train-clean-100  | 25.10 | Jon Scott Jones
-1595 | M | train-other-500  | 30.22 | Riccardo Fasol
-1601 | M | train-other-500  | 13.36 | Michael Yourshaw
-1603 | M | train-clean-360  | 25.16 | Arouet
-1607 | M | train-clean-360  | 24.74 | Claude Banta
-1614 | M | train-other-500  | 30.11 | FNH
-1618 | M | train-other-500  | 23.98 | Nicholas James Bridgewater
-1621 | M | train-other-500  | 28.45 | Caliban
-1624 | M | train-clean-100  | 22.03 | Daniel Shorten
-1629 | F | train-clean-360  | 25.09 | Gwyneth
-1630 | F | dev-other        | 10.06 | spiritualbeing
-1633 | F | train-other-500  | 30.09 | Beecher
-1634 | M | train-clean-360  | 17.65 | daxm
-1636 | F | train-other-500  | 28.48 | Sandra Zera
-1638 | M | train-clean-360  | 23.25 | Kyle M.
-1639 | M | train-clean-360  | 25.07 | Joe Konno
-1641 | F | train-clean-360  | 16.60 | Rohanna
-1643 | M | train-other-500  | 12.85 | Chris Leslie-Hynan
-1645 | M | train-clean-360  | 1.92  | David Shamp
-1646 | M | train-other-500  | 30.03 | Ben Cobbett
-1647 | M | train-other-500  | 30.07 | Rich Meyers
-1648 | F | train-other-500  | 8.01  | Accent
-1649 | F | train-clean-360  | 25.04 | Kalynda
-1650 | M | dev-other        | 10.07 | WangHaojie
-1651 | M | dev-other        | 4.53  | Brendan Hodge
-1653 | F | train-other-500  | 28.72 | Carmina Sansone
-1664 | F | train-other-500  | 27.98 | Shauna M
-1665 | F | train-other-500  | 30.11 | Jessica AC Snyder
-1668 | F | train-clean-360  | 22.96 | stepheather
-1673 | F | dev-clean        | 8.07  | Tonia
-1674 | F | train-other-500  | 30.15 | Jo
-1678 | F | train-clean-360  | 25.08 | leonardswench
-1679 | F | train-other-500  | 22.59 | Polly
-1680 | F | train-other-500  | 18.27 | intothelight
-1681 | F | train-other-500  | 28.90 | islajane
-1685 | M | train-other-500  | 30.05 | Jonathan Horniblow
-1686 | F | dev-other        | 10.11 | neelma
-1688 | M | test-other       | 9.83  | winam
-1690 | F | train-other-500  | 23.47 | Anne-Marie
-1691 | F | train-other-500  | 25.27 | Classicsfan
-1693 | F | train-other-500  | 9.41  | 4Cullen
-1695 | F | train-other-500  | 23.70 | Steph
-1696 | F | train-other-500  | 24.25 | Darcywil
-1699 | M | train-other-500  | 29.66 | Gavin Smith
-1701 | M | dev-other        | 10.03 | camelot2302
-1704 | F | train-other-500  | 19.92 | JB
-1705 | F | train-clean-360  | 23.78 | tittletattle
-1708 | M | train-other-500  | 15.80 | Alaaious
-1710 | F | train-other-500  | 12.02 | Gilly
-1714 | F | train-other-500  | 16.02 | lauralee
-1715 | F | train-other-500  | 24.53 | Marianna
-1717 | F | train-other-500  | 14.81 | PJ
-1721 | F | train-other-500  | 19.66 | Linnea
-1723 | M | train-clean-100  | 25.12 | Rob Whelan
-1724 | F | train-clean-360  | 25.05 | Anna Simon
-1726 | F | train-other-500  | 22.71 | janeite
-1731 | F | train-clean-360  | 25.06 | Dani
-1733 | F | train-other-500  | 22.94 | Mira Cheskis
-1734 | F | train-clean-360  | 15.86 | LuvDemBrooders
-1736 | F | train-other-500  | 29.58 | LC
-1737 | F | train-clean-100  | 25.04 | Erin Hastings
-1740 | F | train-clean-360  | 25.22 | Shubda
-1743 | M | train-clean-100  | 21.29 | Bryan Ness
-1746 | M | train-other-500  | 21.36 | Theo Bacher
-1748 | M | train-clean-360  | 25.16 | Brad Powers
-1750 | F | train-other-500  | 30.09 | Lorie Heinrichs
-1752 | F | train-clean-360  | 22.90 | Jan MacGillivray
-1754 | F | train-clean-360  | 25.08 | Joan Freeman
-1756 | F | train-other-500  | 30.02 | Tamara Hamilton
-1757 | F | train-other-500  | 30.12 | cricket
-1760 | F | train-other-500  | 29.06 | Matthew Howell
-1765 | F | train-other-500  | 22.66 | Kelly Elizabeth
-1767 | F | train-other-500  | 26.17 | Cori Dean
-1769 | M | train-clean-360  | 25.21 | ej
-1772 | M | train-other-500  | 30.11 | David A. Stokely
-1773 | F | train-other-500  | 30.05 | Eliza Horne
-1776 | M | train-clean-360  | 25.17 | Jim Eastman
-1777 | M | train-clean-360  | 25.14 | Professor Chronotis
-1779 | F | train-clean-360  | 18.05 | Cynthia Zocca
-1780 | M | train-other-500  | 18.02 | Micah
-1784 | F | train-other-500  | 21.61 | grovejade
-1789 | M | train-clean-360  | 25.01 | Vin Reilly
-1795 | M | train-other-500  | 17.33 | Muhammad Mussnoon
-1800 | F | train-clean-360  | 25.14 | Scarlett!
-1801 | M | train-clean-360  | 16.59 | Antonio
-1804 | F | train-other-500  | 30.12 | Marie Manis
-1806 | M | train-clean-360  | 25.05 | Gary W. Sherwin
-1809 | F | train-other-500  | 23.28 | cucciasv
-1811 | M | train-clean-360  | 20.41 | Eric Ray
-1813 | F | train-other-500  | 16.81 | tesoro007
-1815 | M | train-other-500  | 27.79 | Aringguth
-1819 | F | train-other-500  | 28.09 | Shannon
-1825 | F | train-clean-360  | 25.00 | srshel
-1826 | M | train-clean-360  | 25.18 | Jacob Miller
-1827 | M | train-clean-360  | 10.55 | Doug Wetzel
-1828 | M | train-other-500  | 28.27 | James Gladwin
-1841 | F | train-clean-100  | 25.13 | Laura Caldwell
-1844 | M | train-other-500  | 23.49 | noonday
-1845 | F | train-clean-360  | 25.15 | Katie Gibboney
-1846 | M | train-other-500  | 30.02 | valikojohn
-1849 | M | train-clean-360  | 25.05 | Kelly Dougherty
-1851 | F | train-clean-360  | 25.03 | Kehinde
-1859 | F | train-clean-360  | 25.17 | Jan Baxter
-1863 | F | train-other-500  | 21.50 | Ania
-1867 | M | train-clean-100  | 25.16 | Rowdy Delaney
-1868 | M | train-other-500  | 30.08 | Graham Redman
-1870 | M | train-other-500  | 29.41 | Stuart Bell
-1874 | M | train-clean-360  | 25.21 | Ernst Schnell
-1878 | M | train-other-500  | 27.47 | BLRossow
-1885 | F | train-clean-360  | 14.38 | inkwelldragon
-1898 | F | train-clean-100  | 25.01 | Jennifer
-1901 | F | train-other-500  | 30.01 | Allyson Hester
-1903 | M | train-clean-360  | 24.21 | Michael Thomas Robinson
-1913 | M | train-clean-360  | 25.03 | Geoff Cowgill
-1914 | M | train-clean-360  | 25.04 | Kevin Kivikko
-1919 | F | dev-clean        | 8.17  | nprigoda
-1920 | F | train-other-500  | 30.05 | Annika Feilbach
-1923 | F | train-clean-360  | 25.05 | Maire Rhode
-1924 | M | train-other-500  | 30.21 | Andrew Drinkwater
-1926 | F | train-clean-100  | 25.19 | Nikki Sullivan
-1931 | F | train-other-500  | 15.74 | poormedea
-1933 | F | train-clean-360  | 14.97 | iremonger
-1938 | M | train-other-500  | 22.02 | icyjumbo (1964-2010)
-1943 | M | train-clean-360  | 23.10 | Corun
-1944 | F | train-clean-360  | 25.03 | Carolyn Frances
-1958 | M | train-clean-360  | 25.17 | Furio
-1961 | F | train-clean-360  | 25.18 | Qhali
-1963 | F | train-clean-100  | 25.19 | Belinda Brown
-1968 | F | train-other-500  | 20.25 | lizzyblack
-1970 | F | train-clean-100  | 25.13 | Dawn Larsen
-1974 | F | train-clean-360  | 25.16 | Katie Baynes
-1977 | F | train-other-500  | 27.80 | Jennie Hughes
-1985 | M | train-other-500  | 24.71 | Jonny Lee
-1987 | M | train-clean-360  | 25.19 | Michael Macedonia
-1988 | F | dev-clean        | 8.16  | Ransom
-1989 | M | train-other-500  | 8.99  | Sergio Baldelli
-1992 | F | train-clean-100  | 12.29 | Michelle White
-1993 | F | dev-clean        | 8.11  | Wendy Belcher
-1995 | F | test-clean       | 8.06  | AJai Hilton
-1998 | F | test-other       | 10.02 | Sonja
-2001 | M | train-other-500  | 19.90 | Phillip David
-2002 | M | train-clean-100  | 24.94 | Larry Maddocks
-2003 | M | train-other-500  | 23.18 | The Penang Lawyer
-2004 | F | train-clean-360  | 25.26 | Kim S
-2007 | F | train-clean-100  | 25.21 | Sheila Morton
-2010 | F | train-clean-360  | 21.53 | Julie Bynum
-2012 | M | train-clean-360  | 25.20 | jburby
-2013 | M | train-other-500  | 30.05 | Mark
-2021 | M | train-other-500  | 30.04 | Keri Ford
-2026 | F | train-other-500  | 29.39 | Mil Nicholson
-2033 | M | test-other       | 10.03 | Filippo Gioachin
-2035 | F | dev-clean        | 8.11  | Sharon Bautista
-2039 | M | train-clean-360  | 25.02 | Anton
-2042 | F | train-other-500  | 29.25 | Charlene V. Smith
-2045 | M | train-clean-360  | 24.96 | David O'Connell
-2046 | M | train-other-500  | 19.01 | kyleti
-2050 | F | train-other-500  | 28.21 | Xe Sands
-2051 | M | train-other-500  | 22.13 | Grant Petersen
-2053 | F | train-clean-360  | 25.03 | Vincent Tapia
-2056 | F | train-clean-360  | 22.72 | Nancy Roberts
-2060 | F | train-clean-360  | 25.02 | Julie Pandya
-2061 | F | train-clean-360  | 25.08 | Jodi Krangle
-2062 | F | train-other-500  | 30.25 | Mindy H
-2063 | M | train-other-500  | 22.02 | hearhis
-2067 | M | train-other-500  | 30.15 | Nick Gisburne
-2068 | F | train-other-500  | 14.75 | Priya, India
-2074 | M | train-clean-360  | 25.06 | Tysto
-2078 | M | dev-clean        | 8.03  | Kathy Caver
-2085 | F | train-clean-360  | 25.09 | Stephanie Dupal-Demartin
-2086 | M | dev-clean        | 8.04  | Nicodemus
-2089 | F | train-other-500  | 30.14 | Martina
-2090 | F | train-other-500  | 26.41 | Melissa
-2092 | F | train-clean-100  | 25.10 | Elaine Hamby
-2093 | M | train-clean-360  | 25.11 | RK Wilcox
-2094 | F | test-clean       | 8.09  | amycsj
-2096 | M | train-other-500  | 15.93 | Nick Marsh
-2100 | F | train-other-500  | 26.76 | Katherine Holt
-2104 | M | train-other-500  | 29.52 | R. S. Steinberg
-2110 | M | train-clean-360  | 25.09 | Aaron Elliott
-2113 | M | train-clean-360  | 24.66 | Andrew Vidal
-2122 | M | train-other-500  | 10.61 | Kenneth R. Morefield
-2127 | M | train-clean-360  | 20.16 | wrongshore
-2133 | M | train-other-500  | 28.63 | Mat Messerschmidt
-2136 | M | train-clean-100  | 25.18 | Great Plains
-2137 | M | train-clean-360  | 25.02 | Jerome Lawsen
-2140 | M | train-other-500  | 27.77 | Ralph Snelson
-2143 | F | train-other-500  | 15.47 | Cat Schirf
-2146 | M | train-clean-360  | 4.89  | Jeff Stuckey
-2148 | F | train-other-500  | 30.17 | BethAnne
-2149 | M | train-clean-360  | 23.08 | Mark Penfold
-2152 | F | train-other-500  | 30.04 | redabrus
-2156 | M | train-clean-360  | 25.18 | Roger Melin
-2159 | M | train-clean-100  | 25.12 | Matthew Westra
-2162 | M | train-clean-360  | 25.10 | Ray Clare
-2167 | M | train-clean-360  | 25.00 | spiderman0521
-2182 | F | train-clean-100  | 25.15 | Susan Umpleby
-2185 | M | train-other-500  | 24.88 | Jonathan Feldman
-2194 | F | train-clean-360  | 25.06 | RobbieRogers
-2195 | M | train-other-500  | 29.06 | Joe Earley
-2196 | F | train-clean-100  | 25.24 | Andrea Fiore
-2198 | M | train-other-500  | 29.82 | Clive Catterall
-2201 | M | train-clean-360  | 25.13 | Stephen Escalera
-2204 | F | train-clean-360  | 25.04 | Pamnache
-2208 | M | train-other-500  | 28.66 | Alan Brown
-2229 | M | train-clean-360  | 21.96 | Pete Williams, Pittsburgh, PA
-2230 | F | train-clean-360  | 25.03 | Isosceles
-2234 | M | train-other-500  | 30.06 | Lars Rolander
-2237 | F | train-other-500  | 23.41 | Chloey Winters
-2238 | M | train-clean-360  | 25.09 | Will Larson
-2240 | M | train-clean-360  | 25.17 | Ralph Volpi
-2246 | M | train-other-500  | 30.11 | RaySee
-2254 | F | train-clean-360  | 25.07 | Heidi Preuss
-2256 | F | train-clean-360  | 25.06 | tamurile
-2262 | M | train-other-500  | 25.53 | Andy
-2269 | F | train-clean-360  | 25.06 | Rhonda Federman
-2270 | F | train-other-500  | 30.08 | Megan Kunkel
-2272 | M | train-clean-360  | 25.18 | Alec Daitsman
-2273 | M | train-other-500  | 22.65 | Peter Kelleher
-2275 | F | train-other-500  | 19.97 | Lori H
-2276 | F | train-other-500  | 30.09 | Andrea
-2277 | F | dev-clean        | 8.01  | zinniz
-2279 | M | train-other-500  | 20.83 | Gilles Lehoux
-2284 | M | train-other-500  | 30.20 | Zapo
-2285 | M | train-clean-360  | 25.16 | Bob Sage
-2288 | F | train-other-500  | 30.13 | Ellis Christoff
-2289 | M | train-clean-100  | 25.08 | David Kleparek
-2292 | M | train-other-500  | 30.17 | Dick Durette
-2294 | M | train-clean-360  | 25.24 | James Christopher
-2297 | F | train-other-500  | 30.20 | Philippa
-2299 | M | train-clean-360  | 25.23 | cpalmer17
-2300 | M | test-clean       | 8.19  | Mitchell L Leopard
-2301 | F | train-other-500  | 30.09 | Chris Jones
-2309 | M | train-other-500  | 30.03 | Wyatt
-2312 | F | train-other-500  | 30.22 | Lucy Lo Faro
-2319 | M | train-clean-360  | 25.21 | Jack Farrell
-2334 | M | train-clean-360  | 25.03 | David Lipa
-2339 | F | train-other-500  | 30.09 | skellie
-2341 | M | train-other-500  | 30.04 | webround
-2346 | F | train-other-500  | 17.91 | FirstKnight
-2348 | F | train-clean-360  | 22.53 | KellyLC
-2351 | F | train-other-500  | 30.08 | hugoceline
-2356 | M | train-other-500  | 30.20 | Paul Curran
-2361 | F | train-other-500  | 30.24 | M. J. Boyle
-2364 | F | train-clean-360  | 23.86 | Anna-Maria Viola
-2368 | M | train-clean-360  | 25.08 | Alex C. Telander
-2374 | F | train-other-500  | 21.75 | M.C.Y.
-2380 | M | train-other-500  | 17.00 | Jacob Cherry
-2384 | M | train-clean-100  | 20.21 | Ger
-2388 | M | train-clean-360  | 25.01 | Greg Bell
-2391 | F | train-clean-100  | 24.54 | treefingers
-2393 | M | train-clean-360  | 22.63 | Michael Bradford
-2397 | M | train-clean-360  | 25.14 | texttalker
-2401 | F | train-clean-360  | 21.95 | Matt Warzel
-2404 | M | train-clean-360  | 25.21 | n8evv
-2405 | M | train-other-500  | 30.11 | musil
-2407 | M | train-other-500  | 28.95 | ajmacbeth
-2411 | F | train-clean-360  | 25.04 | kristiface
-2412 | F | dev-clean        | 8.06  | calystra
-2414 | M | test-other       | 10.10 | Ashwin Jain
-2416 | F | train-clean-100  | 25.17 | Julia Albath
-2427 | M | train-clean-360  | 25.02 | Ed Meade
-2428 | M | dev-clean        | 8.02  | Stephen Kinford
-2436 | M | train-clean-100  | 25.15 | Seth Adam Sher
-2437 | M | train-other-500  | 21.23 | Wetcoast
-2445 | F | train-other-500  | 13.04 | musici123
-2448 | M | train-other-500  | 23.07 | David Federman
-2473 | M | train-clean-360  | 25.16 | Hoosemon
-2481 | F | train-clean-360  | 25.07 | Alana Jordan
-2485 | F | train-other-500  | 30.03 | Serin
-2487 | F | train-other-500  | 28.17 | Rachel Lintern
-2488 | F | train-other-500  | 30.16 | Lisa Wilson
-2491 | M | train-other-500  | 20.67 | johnb
-2494 | M | train-clean-360  | 25.03 | Mark Cawley
-2496 | M | train-other-500  | 30.10 | Ben Dutton
-2498 | F | train-clean-360  | 25.05 | B. Grebe
-2499 | M | train-clean-360  | 25.14 | Paul Henry Tremblay
-2504 | F | train-other-500  | 14.22 | Helen Elsbeth
-2506 | F | dev-other        | 8.52  | Julie VW
-2512 | F | train-clean-360  | 24.05 | Marion
-2514 | M | train-clean-100  | 25.11 | S. Young
-2517 | M | train-clean-360  | 25.25 | Gayland Darnell
-2518 | M | train-clean-100  | 25.06 | Rob Powell
-2522 | F | train-other-500  | 23.34 | senshisteph
-2526 | M | train-other-500  | 30.09 | Bob Gilham
-2531 | M | train-clean-360  | 25.12 | Greg Weeks
-2532 | F | train-clean-360  | 25.11 | Jennifer Lott
-2533 | M | train-clean-360  | 15.81 | Steven Proctor
-2541 | M | train-other-500  | 30.04 | Eddie Winter
-2544 | F | train-other-500  | 30.02 | Annise
-2545 | M | train-other-500  | 15.29 | the quiet fox
-2552 | M | train-other-500  | 28.82 | Daniel Cranston
-2553 | F | train-other-500  | 30.04 | daisy55
-2562 | M | train-clean-360  | 25.19 | Scott Merrill
-2568 | F | train-other-500  | 30.07 | Elena the Quiet
-2570 | F | train-clean-360  | 25.05 | kindlibrarian
-2573 | F | train-clean-360  | 25.00 | Becca B
-2574 | M | train-other-500  | 28.34 | TimSC
-2577 | F | train-clean-360  | 25.16 | K Hindall
-2581 | F | train-clean-360  | 25.12 | Julie Levi
-2582 | F | train-clean-360  | 25.16 | dolce
-2587 | F | train-other-500  | 30.13 | Anne Cheng
-2588 | M | train-other-500  | 26.44 | Padraig O'hIceadha
-2589 | M | train-clean-360  | 25.04 | Jordan
-2592 | F | train-clean-360  | 20.82 | Anna Roberts
-2598 | F | train-clean-360  | 23.05 | Barbara Bulkeley
-2606 | M | train-other-500  | 30.16 | Marc Tanti
-2607 | F | train-other-500  | 30.11 | Ruth Golding
-2609 | M | test-other       | 10.10 | Ian Hatley
-2618 | M | train-clean-360  | 24.51 | Phil Surette
-2624 | M | train-other-500  | 30.05 | David Nicol
-2625 | F | train-other-500  | 20.53 | Auntie Em
-2628 | M | train-clean-360  | 25.22 | Zloot
-2638 | F | train-clean-360  | 25.02 | Dawn
-2652 | F | train-clean-360  | 15.01 | MixieArmadillo
-2654 | M | train-clean-360  | 16.45 | Notelrac
-2660 | M | train-other-500  | 30.01 | mpetranech
-2671 | F | train-other-500  | 21.49 | Foreign Girl
-2673 | M | train-clean-360  | 25.06 | jude kaider
-2674 | M | train-clean-360  | 24.32 | Jason Procopio
-2676 | F | train-other-500  | 27.44 | missizii
-2688 | M | train-clean-360  | 25.15 | Christopher Jennings
-2691 | F | train-clean-100  | 25.16 | Donna Stewart
-2694 | F | train-other-500  | 29.43 | DianaJMB
-2696 | M | train-clean-360  | 15.92 | anonymous
-2709 | F | train-clean-360  | 17.49 | PopularOutcast
-2712 | F | train-other-500  | 27.11 | anoldfashiongirl
-2724 | M | train-other-500  | 20.73 | Michael Dalling
-2730 | F | train-other-500  | 24.25 | Shirley Anderson
-2733 | M | train-other-500  | 15.37 | CalmDragon
-2735 | F | train-other-500  | 14.04 | ASchindler
-2740 | M | train-other-500  | 22.19 | Tony Ashworth
-2741 | F | train-clean-360  | 25.21 | Jan Dawn Doronila
-2748 | F | train-other-500  | 24.78 | Annoying Twit
-2751 | F | train-clean-360  | 24.39 | Angela Kelley
-2754 | F | train-other-500  | 30.12 | peaceuntoyou
-2758 | F | train-clean-360  | 25.19 | SopranoHarmony
-2762 | F | train-other-500  | 30.01 | Phillipa Chantry
-2764 | F | train-clean-100  | 25.19 | Piper Hale
-2769 | M | train-clean-360  | 25.25 | Anthony Wilson
-2774 | M | train-clean-360  | 25.14 | William Kevin Manire
-2775 | F | train-clean-360  | 25.17 | ink tree
-2785 | F | train-clean-360  | 25.23 | humeangel
-2787 | M | train-clean-360  | 19.66 | Quentin Manuel
-2790 | F | train-clean-360  | 25.09 | Victoria Slonosky
-2792 | F | train-other-500  | 30.16 | Varra Unreal
-2803 | M | dev-clean        | 8.20  | aquielisunari
-2812 | M | train-clean-360  | 14.63 | Greg Hartley
-2815 | M | train-clean-360  | 15.98 | Michael Sample
-2816 | M | train-clean-360  | 25.19 | Andrew Symons
-2817 | F | train-clean-100  | 25.14 | Catherine Millward
-2823 | M | train-clean-360  | 18.78 | ChrisC
-2825 | M | train-other-500  | 30.02 | Ernst Pattynama
-2827 | M | train-clean-360  | 21.49 | David Leeson
-2830 | M | test-clean       | 8.04  | Tim Perkins
-2834 | F | train-other-500  | 30.02 | Ksushi
-2836 | F | train-clean-100  | 25.12 | Linda McDaniel
-2843 | M | train-clean-100  | 25.18 | ricell
-2853 | F | train-clean-360  | 25.00 | Jane Greensmith
-2854 | M | train-other-500  | 30.25 | Andrew Coleman
-2882 | F | train-clean-360  | 23.84 | ayngelwing
-2893 | M | train-clean-100  | 24.41 | Ryan Sutter
-2895 | F | train-other-500  | 24.14 | Jenny Lundak
-2902 | M | dev-clean        | 8.10  | dexter
-2909 | F | train-other-500  | 30.13 | Petra
-2910 | F | train-clean-100  | 22.33 | Janna
-2911 | M | train-clean-100  | 25.10 | David Lawrence
-2919 | F | train-other-500  | 30.17 | Raerity
-2920 | F | train-clean-360  | 25.18 | Rechelle
-2925 | F | train-other-500  | 26.47 | Darla
-2929 | M | train-clean-360  | 25.15 | Topaz
-2930 | M | train-other-500  | 20.36 | BUAES
-2943 | F | train-other-500  | 30.09 | Sarah Gutierrez
-2946 | M | train-other-500  | 19.68 | Markus Wachenheim
-2952 | M | train-clean-100  | 25.02 | Scott Carpenter
-2960 | M | train-clean-360  | 25.03 | Scotty
-2961 | F | test-clean       | 8.07  | Leni
-2967 | F | train-other-500  | 30.26 | Landii
-2971 | M | train-clean-360  | 25.01 | Matthew C. Heckel
-2975 | F | train-other-500  | 26.46 | Sarafina Suransky
-2979 | F | train-other-500  | 18.85 | Jilliane Brandt
-2985 | M | train-other-500  | 21.98 | Cantor
-2988 | F | train-other-500  | 30.16 | Larissa Little
-2989 | F | train-clean-100  | 25.21 | Jamie Strassenburg, Cypress, California
-2990 | M | train-other-500  | 30.07 | Tom Crawford
-2992 | M | train-clean-360  | 24.13 | davechase
-2997 | F | train-other-500  | 30.26 | Violet
-2998 | M | train-other-500  | 30.04 | Kim Jansen
-2999 | M | train-clean-360  | 25.16 | Joseph Finkberg
-3000 | M | dev-clean        | 8.03  | Brian von Dedenroth
-3001 | F | train-clean-360  | 21.97 | priscilla hewitt
-3003 | F | train-clean-360  | 25.09 | Sue Anderson
-3005 | M | test-other       | 10.04 | Ken Tischler
-3006 | F | train-other-500  | 30.14 | MorganScorpion
-3008 | F | train-clean-360  | 25.17 | Gloria Zbilicki
-3009 | M | train-clean-360  | 25.00 | Jim Ruddy
-3020 | M | train-other-500  | 30.07 | Mike Vendetti
-3021 | M | train-other-500  | 29.27 | leetcr
-3025 | F | train-clean-360  | 25.07 | MichelleHarris
-3032 | M | train-clean-360  | 25.10 | Utek
-3033 | F | train-other-500  | 30.09 | valli
-3045 | M | train-other-500  | 27.99 | Cameron Conaway
-3046 | F | train-clean-360  | 25.21 | Stephanie Land
-3053 | F | train-other-500  | 20.98 | Tracy Yonemoto
-3054 | M | train-other-500  | 23.13 | GerryR
-3060 | M | train-other-500  | 26.15 | Didier
-3063 | M | train-other-500  | 25.29 | markman
-3070 | M | train-clean-360  | 25.15 | Mike Schwabe
-3072 | M | train-clean-360  | 25.05 | Chris Amos
-3079 | F | train-other-500  | 7.10  | nivedita
-3080 | F | test-other       | 10.13 | breathe
-3081 | F | dev-clean        | 8.00  | Renata
-3082 | M | train-clean-360  | 17.05 | Logan McCamon
-3083 | F | train-clean-360  | 25.06 | Emily Jarmard
-3088 | M | train-other-500  | 14.21 | Sean O'Hara
-3090 | F | train-other-500  | 22.39 | kelcymx
-3092 | F | train-clean-360  | 25.06 | Maggie Russell
-3094 | F | train-clean-360  | 25.10 | moe
-3097 | M | train-other-500  | 28.71 | nitram
-3098 | M | train-other-500  | 28.85 | woggy298
-3100 | M | train-other-500  | 29.56 | David Higham
-3105 | M | train-clean-360  | 25.20 | gfairch511
-3109 | M | train-other-500  | 30.20 | Diogenes Dog
-3112 | F | train-clean-100  | 25.08 | Jessica Louise
-3114 | M | train-clean-360  | 25.04 | Geoffrey Edwards
-3118 | M | train-clean-360  | 25.06 | George Yeager
-3119 | F | train-clean-360  | 25.18 | Sharon Riskedahl
-3125 | F | train-other-500  | 30.23 | Fran
-3132 | M | train-other-500  | 29.97 | Andy Yu
-3135 | M | train-other-500  | 20.03 | Steve Foreman
-3137 | M | train-other-500  | 20.23 | Parrot
-3138 | F | train-other-500  | 3.53  | Labyrinth Composer
-3142 | M | train-other-500  | 15.12 | RogerA
-3143 | F | train-other-500  | 23.59 | suzanne
-3144 | M | train-other-500  | 30.01 | jfmarchini
-3148 | M | train-other-500  | 18.41 | artos
-3157 | F | train-clean-360  | 25.05 | TriciaG
-3168 | M | train-clean-100  | 23.90 | David Anton
-3170 | M | dev-clean        | 8.10  | VOICEGUY
-3171 | M | train-clean-360  | 25.11 | JoeD
-3172 | F | train-other-500  | 29.24 | Beatrice
-3179 | F | train-other-500  | 26.80 | Robin
-3180 | M | train-clean-360  | 25.01 | Mike Conrad
-3185 | M | train-clean-360  | 25.02 | JohnNewman
-3187 | M | train-clean-360  | 24.32 | Wasichu
-3192 | M | train-other-500  | 30.07 | Arfuhrm
-3196 | F | train-other-500  | 30.19 | Elizabeth Harker
-3214 | M | train-clean-100  | 25.08 | fourteatoo
-3215 | F | train-clean-360  | 25.02 | Shirley Ellen
-3221 | M | train-clean-360  | 25.12 | David Schoepf
-3224 | F | train-clean-360  | 25.19 | Acacia Wood
-3227 | F | train-other-500  | 17.26 | Betina
-3228 | F | train-clean-360  | 25.01 | Benuathanasia
-3230 | M | train-clean-360  | 25.13 | rasputin
-3235 | F | train-clean-100  | 24.94 | Karen Commins
-3238 | M | train-other-500  | 19.07 | nihilist00
-3240 | M | train-clean-100  | 25.09 | flakker
-3242 | M | train-clean-100  | 25.03 | peac
-3244 | F | train-other-500  | 30.09 | wilwarin
-3245 | F | train-other-500  | 30.10 | wendy
-3257 | M | train-other-500  | 30.17 | Jay Vance
-3258 | F | train-clean-360  | 23.50 | mwalimu
-3259 | F | train-clean-100  | 25.19 | Kate West
-3261 | M | train-other-500  | 16.03 | fourgrays
-3268 | M | train-other-500  | 30.06 | Patrick McHaffie
-3271 | M | train-other-500  | 19.99 | Ancient mariner
-3272 | M | train-other-500  | 19.89 | Matthew_J_Almeida
-3274 | M | train-clean-360  | 25.26 | Morgan Saletta
-3285 | M | train-other-500  | 30.03 | David Collins
-3288 | M | train-other-500  | 30.09 | Euan Bayliss
-3289 | M | train-clean-360  | 25.06 | lukeprog
-3290 | F | train-other-500  | 29.75 | Marian Martin
-3294 | F | train-clean-360  | 19.45 | Marcy Fraser
-3307 | M | train-clean-360  | 25.06 | Doug Allison
-3314 | F | train-other-500  | 30.14 | Carol Stripling
-3318 | F | train-other-500  | 30.19 | Magdalena
-3319 | F | train-other-500  | 28.56 | mjbrichant
-3328 | M | train-clean-360  | 25.16 | Al Dano
-3330 | F | train-clean-360  | 25.10 | B. G. Oxford
-3331 | F | test-other       | 10.08 | Peggy
-3334 | F | train-other-500  | 14.57 | joi
-3340 | M | train-clean-360  | 25.12 | Preston McConkie
-3346 | F | train-other-500  | 30.03 | Hannah Dowell
-3347 | F | train-clean-360  | 25.23 | tornadogrrrl
-3356 | F | train-other-500  | 20.03 | Diana Solomon
-3357 | F | train-clean-360  | 24.37 | swade
-3361 | F | train-clean-360  | 25.19 | Linda Lee Paquet
-3368 | M | train-clean-360  | 25.19 | Jim Allman
-3370 | M | train-clean-360  | 25.16 | Glenn Simonsen
-3373 | M | train-other-500  | 18.98 | cvd
-3374 | M | train-clean-100  | 25.17 | Craig Campbell
-3379 | F | train-clean-360  | 25.09 | Kele
-3380 | F | train-clean-360  | 25.06 | DrBeccaAnne
-3381 | M | train-other-500  | 2.99  | Cagliostro
-3389 | M | train-clean-360  | 25.25 | von
-3394 | F | train-other-500  | 30.03 | Jackie Provau
-3400 | F | train-other-500  | 17.98 | Laura Davis
-3409 | F | train-other-500  | 30.24 | Philippa Brodie
-3411 | F | train-other-500  | 28.43 | SuD
-3417 | M | train-other-500  | 17.97 | Albatross
-3433 | M | train-other-500  | 30.24 | Bob Sherman
-3436 | M | train-clean-100  | 25.25 | Anders Lankford
-3440 | F | train-clean-100  | 25.18 | Heidi Will
-3446 | F | train-clean-360  | 25.24 | kayo
-3448 | M | train-clean-360  | 25.24 | Todd Lennon
-3465 | F | train-other-500  | 29.59 | ravenotation
-3467 | F | train-other-500  | 30.23 | js392
-3470 | M | train-other-500  | 30.11 | Jason Mills
-3479 | F | train-other-500  | 30.09 | Karan Yamada
-3482 | F | train-clean-360  | 25.22 | Hayden
-3483 | M | train-clean-360  | 25.20 | Alan Winterrowd
-3486 | M | train-clean-100  | 25.08 | Robin Balmer
-3488 | M | train-other-500  | 17.57 | Tom Weiss
-3490 | M | train-clean-360  | 25.10 | Gregg Margarite (1957-2012)
-3493 | F | train-clean-360  | 21.49 | Gail Mattern
-3500 | F | train-other-500  | 28.86 | B. Treadgold
-3503 | M | train-other-500  | 28.15 | Christian Al-Kadi
-3513 | F | train-clean-360  | 25.01 | Symmie
-3521 | M | train-clean-360  | 25.07 | NickNumber
-3526 | F | train-clean-100  | 25.18 | Bereni
-3528 | F | test-other       | 10.26 | minimoose83
-3536 | F | dev-clean        | 8.15  | Arielle Lipshaw
-3537 | F | train-clean-360  | 25.10 | Tracy Datlen
-3538 | F | test-other       | 10.03 | Wyndelyn
-3540 | M | train-clean-360  | 24.55 | Bill Ruhsam
-3541 | M | train-other-500  | 30.14 | Termin Dyan
-3546 | F | train-clean-360  | 25.21 | Jeannie
-3547 | M | train-other-500  | 30.11 | BenW
-3549 | F | train-clean-360  | 25.05 | Katie Riley
-3551 | F | train-clean-360  | 25.10 | Annie Kirkpatrick
-3553 | M | train-other-500  | 25.03 | Luc Kordas
-3554 | F | train-other-500  | 30.06 | LaraC, Louisville, KY
-3557 | F | train-other-500  | 30.08 | Rachel Triska
-3559 | F | train-other-500  | 30.06 | Kerry Hiles
-3564 | F | train-other-500  | 24.04 | Vanessa
-3567 | M | train-other-500  | 30.12 | David Lazarus
-3570 | F | test-clean       | 8.05  | sarac
-3571 | M | train-other-500  | 23.68 | HarryInk
-3575 | F | test-clean       | 8.06  | supergirl
-3576 | F | dev-clean        | 8.00  | JudyGibson
-3584 | F | train-clean-360  | 22.30 | Bridget Gaige
-3587 | M | train-other-500  | 25.46 | John Nixon
-3588 | F | train-other-500  | 30.06 | Neeru Iyer
-3592 | M | train-other-500  | 10.77 | Paul McCartan
-3595 | M | train-other-500  | 30.07 | Martin Geeson
-3598 | F | train-other-500  | 26.07 | Dawn
-3606 | M | train-other-500  | 28.34 | Ashwath Ganesan
-3607 | M | train-clean-100  | 22.10 | Richard Wallis
-3615 | F | train-clean-360  | 25.05 | Lucy Perry
-3618 | M | train-other-500  | 26.32 | Timothy Ferguson
-3630 | F | train-clean-360  | 12.45 | Rachel Gatwood
-3638 | F | train-clean-360  | 25.16 | AmyG
-3641 | F | train-other-500  | 25.50 | Joelle Peebles
-3645 | F | train-clean-360  | 25.08 | MaryAnn
-3647 | F | train-other-500  | 30.24 | Channe
-3650 | M | train-other-500  | 23.18 | Jonathan Ross
-3654 | M | train-clean-360  | 25.09 | Mark Wilson
-3656 | M | train-other-500  | 30.26 | Kai Lu
-3657 | M | train-other-500  | 30.03 | Bellona Times
-3660 | M | dev-other        | 10.26 | Russ Clough
-3663 | F | dev-other        | 10.13 | ppezz
-3664 | M | train-clean-100  | 25.06 | Barry Eads
-3665 | M | train-other-500  | 24.95 | C.J. Casey
-3675 | F | train-other-500  | 26.63 | Linda Ferguson
-3679 | F | train-other-500  | 20.97 | veronasser
-3681 | F | train-other-500  | 30.09 | Ann Boulais
-3686 | M | train-clean-360  | 25.07 | Doug Delisle
-3691 | M | train-other-500  | 29.41 | Hollis Hanover
-3698 | F | train-other-500  | 30.04 | Nadine Eckert-Boulet
-3699 | M | train-clean-100  | 25.15 | Bruce Pirie
-3703 | F | train-clean-360  | 25.06 | Linda Andrus
-3717 | M | train-clean-360  | 25.09 | Elliott Miller
-3723 | M | train-clean-100  | 25.07 | Kevin Lavin
-3728 | F | train-clean-360  | 25.17 | amycs
-3729 | F | test-clean       | 8.03  | Heather Hogan
-3733 | F | train-clean-360  | 25.01 | Melanie Schleeter McCalmont
-3738 | F | train-clean-360  | 25.01 | Collee McKinnon
-3744 | M | train-other-500  | 28.62 | Jonathan Burchard
-3747 | M | train-other-500  | 15.39 | Keith Henige
-3752 | M | dev-clean        | 8.06  | Mark Welch
-3757 | F | train-other-500  | 18.90 | EmAllise
-3764 | F | test-other       | 10.12 | Gabrielle Lambrick
-3779 | F | train-other-500  | 30.11 | Rachel Steely
-3780 | M | train-other-500  | 29.90 | Pete
-3781 | F | train-clean-360  | 25.02 | Celena Arter
-3783 | M | train-other-500  | 16.30 | TexasSteve
-3790 | F | train-clean-360  | 25.10 | hpark
-3792 | M | train-clean-360  | 16.38 | Brian Keith Barnes
-3793 | M | train-other-500  | 30.18 | Nathan
-3796 | M | train-other-500  | 26.72 | Mario Pineda
-3798 | F | train-other-500  | 30.22 | Bianca Kramer
-3807 | M | train-clean-100  | 22.31 | Jesse Noar
-3816 | F | train-clean-360  | 25.05 | Bev J Stevens
-3819 | M | train-other-500  | 30.04 | StarrDog
-3825 | M | train-clean-360  | 17.60 | Matt Wills
-3830 | M | train-clean-100  | 24.01 | rymd80
-3835 | M | train-clean-360  | 18.92 | M.White
-3843 | F | train-other-500  | 30.16 | storm
-3845 | M | train-other-500  | 30.22 | Ray Smith
-3848 | F | train-other-500  | 28.82 | skoval
-3851 | F | train-clean-360  | 25.26 | mbousquet
-3852 | F | train-clean-360  | 22.37 | selniff
-3853 | F | dev-clean        | 8.05  | M. Bertke
-3857 | M | train-clean-100  | 25.17 | Epistomolus
-3864 | M | train-clean-360  | 24.20 | Tom Watts
-3866 | M | train-clean-360  | 25.12 | SilverG
-3867 | F | train-other-500  | 28.37 | Roberta Carlisle
-3869 | M | train-clean-360  | 25.02 | Timothy Pinkham
-3871 | M | train-other-500  | 29.98 | Figura
-3876 | F | train-clean-360  | 25.03 | Frances Marcinkiewicz
-3879 | F | train-clean-100  | 25.10 | Keneva
-3885 | F | train-other-500  | 24.69 | Elli
-3889 | F | train-clean-360  | 25.07 | Alina
-3894 | M | train-other-500  | 18.14 | Indy Gosal
-3895 | M | train-other-500  | 13.98 | porlob
-3896 | M | train-other-500  | 30.23 | Will Zufall
-3905 | F | train-clean-360  | 23.23 | J. Rebecca Franklin
-3906 | F | train-other-500  | 30.04 | tabithat
-3909 | F | train-other-500  | 30.03 | Evelyn Clarke
-3911 | F | train-other-500  | 18.56 | Joseph Couves
-3912 | M | train-other-500  | 30.18 | Bob Neufeld
-3914 | M | train-clean-360  | 25.22 | Rob James
-3915 | F | dev-other        | 10.32 | JenniferW
-3922 | F | train-clean-360  | 25.20 | Ashley Candland
-3923 | M | train-clean-360  | 25.10 | Sean Michael Hogan
-3925 | F | train-other-500  | 14.78 | Viglione
-3926 | F | train-other-500  | 29.85 | Denise Lacey
-3927 | M | train-clean-360  | 25.16 | Bob Stretch
-3928 | F | train-other-500  | 30.11 | Marianne Coleman-Hipkins
-3934 | F | train-other-500  | 21.35 | Lynne Carroll
-3945 | M | train-clean-360  | 22.57 | Floyd Wilde
-3947 | F | train-clean-100  | 22.43 | johnell
-3955 | M | train-other-500  | 24.95 | Fredrik Karlsson
-3959 | M | train-other-500  | 30.24 | rjhargrav
-3962 | M | train-other-500  | 25.43 | writerboyontour
-3967 | F | train-clean-360  | 21.86 | Christine Dufour
-3969 | F | train-other-500  | 30.01 | CM Slosson
-3972 | F | train-clean-360  | 25.07 | Joy Easton
-3977 | M | train-clean-360  | 25.11 | LivelyHive
-3979 | F | train-clean-360  | 25.14 | Dale A. Bade
-3982 | F | train-clean-100  | 25.07 | Kate Adams
-3983 | F | train-clean-100  | 25.01 | lavocedorata
-3989 | M | train-clean-360  | 25.06 | Rayne
-3990 | F | train-other-500  | 28.44 | Jessi
-3992 | F | train-other-500  | 30.24 | perpetualdreamworld
-3994 | F | train-clean-360  | 25.09 | Miriam Esther Goldman
-3997 | F | test-other       | 10.31 | Sophia Choi
-4005 | F | train-other-500  | 30.14 | Jhiu
-4009 | F | train-other-500  | 29.26 | Diana Majlinger
-4010 | M | train-clean-360  | 25.19 | David Baldwin
-4013 | M | train-clean-360  | 25.14 | Kevin Maxson
-4014 | M | train-clean-100  | 25.20 | Tom Clifton
-4015 | M | train-other-500  | 30.01 | JimOCR
-4017 | M | train-other-500  | 28.96 | gsolgaard
-4018 | M | train-clean-100  | 25.17 | Nicholas Clifford
-4019 | M | train-other-500  | 27.31 | brrrrrr6
-4020 | M | train-other-500  | 17.91 | Linda
-4021 | F | train-other-500  | 30.21 | Linda Woods
-4034 | F | train-other-500  | 30.24 | Sienna
-4039 | M | train-clean-360  | 25.15 | Shawn Craig Smith
-4042 | M | train-other-500  | 30.02 | Ryan DeRamos
-4044 | F | train-clean-360  | 23.50 | serenitylee
-4051 | F | train-clean-100  | 25.14 | Liz Devens
-4054 | M | train-clean-360  | 15.16 | Ryan Gubele
-4057 | F | train-clean-360  | 25.26 | RoseA
-4059 | M | train-other-500  | 29.84 | Troy Bond
-4063 | F | train-other-500  | 16.23 | Abigail Bartels
-4064 | F | train-clean-360  | 25.12 | Margaret Espaillat
-4071 | F | train-clean-360  | 25.04 | Nichelle von Lauder
-4077 | M | test-clean       | 8.14  | Nathan Markham
-4078 | M | train-other-500  | 21.58 | Richard Kilmer
-4085 | M | train-other-500  | 18.85 | Paul P Miller
-4088 | F | train-clean-100  | 25.15 | Blazin48
-4090 | F | train-other-500  | 25.53 | madmouth
-4098 | F | train-clean-360  | 25.04 | Rachell Lovett
-4104 | F | train-other-500  | 28.44 | Linda Dodge
-4108 | M | train-clean-360  | 25.21 | garymacf
-4110 | M | train-clean-360  | 25.18 | Richard Ellwood
-4111 | F | train-clean-360  | 25.07 | Rachel P.
-4116 | F | train-clean-360  | 25.06 | Amy Benton
-4122 | M | train-other-500  | 29.44 | Wendel Topper
-4133 | M | train-clean-360  | 24.12 | BigStory
-4137 | F | train-clean-100  | 24.92 | Sarah LuAnn
-4138 | F | train-clean-360  | 21.69 | daltongirl
-4145 | F | train-clean-360  | 25.20 | Patti Cunningham
-4148 | M | train-clean-360  | 25.18 | David A. Moore.
-4152 | M | train-clean-360  | 25.18 | Ata Khudayberdiev
-4153 | F | dev-other        | 10.01 | Hilara
-4156 | F | train-other-500  | 30.03 | Caroline Shapiro
-4160 | F | train-clean-100  | 25.09 | Rosie
-4161 | M | train-other-500  | 20.59 | Daniel Paashaus
-4172 | M | train-other-500  | 29.53 | Graeme Dunlop
-4174 | F | train-other-500  | 30.01 | Availle
-4179 | M | train-other-500  | 30.18 | Robert Keiper
-4189 | F | train-other-500  | 28.25 | Megan Argo
-4191 | M | train-other-500  | 22.03 | Grant Hurlock
-4192 | F | train-other-500  | 18.59 | MaryModern
-4193 | M | train-other-500  | 30.23 | Ethan Rampton
-4195 | F | train-clean-100  | 25.08 | bj
-4196 | M | train-other-500  | 30.09 | Jim Clevenger
-4198 | M | test-other       | 10.14 | Examinfo
-4205 | M | train-other-500  | 22.17 | psi_mon
-4211 | F | train-other-500  | 27.31 | A. Knight
-4214 | F | train-clean-100  | 17.45 | A. Janelle Risa
-4216 | F | train-other-500  | 30.02 | Hannah Skoonberg
-4217 | M | train-other-500  | 30.06 | Brendan Tannam
-4218 | M | train-other-500  | 29.74 | David Cole
-4222 | F | train-clean-360  | 25.22 | msjodi777
-4225 | F | train-other-500  | 26.78 | gmiteva
-4226 | M | train-clean-360  | 25.12 | Jud Niven
-4234 | F | train-other-500  | 30.02 | laineyben
-4235 | F | train-other-500  | 28.44 | Haylayer Flaga
-4236 | M | train-clean-360  | 16.01 | Nicholas Feulner
-4238 | F | train-clean-360  | 24.09 | Chela
-4243 | M | train-clean-360  | 25.16 | Ian Grae
-4246 | M | train-clean-360  | 15.61 | JLaddJr
-4257 | M | train-clean-360  | 22.42 | garbageman99
-4260 | F | train-clean-360  | 23.54 | Clacas
-4262 | F | train-other-500  | 23.66 | LoraBeth Davis
-4263 | F | train-other-500  | 29.88 | Chelsea Baker
-4267 | M | train-clean-100  | 25.14 | Ric F
-4273 | F | train-other-500  | 13.25 | as101
-4277 | M | train-other-500  | 19.92 | JohanG
-4278 | M | train-clean-360  | 25.07 | smhamon
-4280 | F | train-other-500  | 27.64 | BB
-4289 | F | train-clean-360  | 22.49 | Veronica Jenkins
-4290 | M | train-clean-360  | 25.01 | Joshua Paul Johnson
-4294 | F | test-other       | 10.17 | Jessamy Gloor
-4295 | M | train-other-500  | 30.17 | Slawek
-4297 | F | train-clean-100  | 25.04 | Tina Horning
-4305 | F | train-other-500  | 18.60 | Aspergine
-4310 | F | train-other-500  | 10.29 | grace4him
-4313 | F | train-other-500  | 29.54 | May Low
-4321 | F | train-other-500  | 30.03 | Xenutia
-4323 | F | dev-other        | 10.02 | BookAngel7
-4327 | F | train-other-500  | 30.01 | brokenaltar
-4331 | F | train-clean-360  | 24.77 | Barbara Clements
-4335 | M | train-clean-360  | 21.74 | davidb
-4340 | F | train-clean-100  | 21.97 | kiwafruit
-4344 | M | train-other-500  | 26.88 | Tadhg
-4345 | M | train-other-500  | 23.32 | Richard Schipper
-4350 | M | test-other       | 10.12 | willem
-4352 | F | train-other-500  | 29.82 | Martina Hutchins
-4356 | F | train-clean-360  | 25.07 | Patricia Rutledge
-4358 | M | train-clean-360  | 25.11 | Kim Stich
-4362 | F | train-clean-100  | 25.18 | Michelle Montano
-4363 | F | train-clean-360  | 25.04 | Emily Livingston
-4379 | M | train-other-500  | 30.01 | Scott Dahlem
-4381 | F | train-clean-360  | 6.58  | Denise Resnik
-4396 | F | train-other-500  | 30.07 | Michael Wolf
-4397 | M | train-clean-100  | 25.06 | John Dennison
-4402 | M | train-other-500  | 22.67 | Christopher Sanner
-4406 | M | train-clean-100  | 25.12 | Matthew Scott Surprenant
-4407 | M | train-other-500  | 21.73 | Ted Drury
-4411 | F | train-other-500  | 27.13 | Zarnaz
-4415 | F | train-other-500  | 12.54 | Anita Fleming
-4420 | M | train-other-500  | 23.18 | Steve Lomas
-4422 | F | train-other-500  | 30.05 | Kamna
-4423 | M | train-other-500  | 25.86 | David Dwight
-4425 | M | train-clean-360  | 17.73 | Gary Coy
-4427 | F | train-clean-360  | 25.06 | Lynne Handler
-4428 | M | train-other-500  | 20.43 | Blueoyster101
-4433 | M | train-clean-360  | 12.83 | Ken Sterry
-4434 | F | train-clean-360  | 25.03 | Sarah Nuxoll
-4438 | M | train-clean-360  | 25.00 | Greg W.
-4441 | M | train-clean-100  | 25.06 | William Peck
-4442 | M | train-other-500  | 27.21 | Pep
-4443 | F | train-other-500  | 30.09 | THOVO
-4446 | F | test-clean       | 8.00  | Jen Maxwell
-4447 | M | train-other-500  | 30.14 | Cascades
-4455 | F | train-other-500  | 30.00 | Patrick Wells
-4463 | F | train-other-500  | 29.36 | Cate Mackenzie
-4474 | M | train-other-500  | 28.95 | John L. Clark
-4480 | M | train-other-500  | 30.03 | Iskander Shafikov
-4481 | F | train-clean-100  | 22.56 | margo zinberg
-4484 | M | train-other-500  | 30.23 | Phil Griffiths
-4487 | F | train-other-500  | 30.19 | Nienke
-4490 | F | train-clean-360  | 25.01 | Rachel Weaver
-4492 | F | train-other-500  | 30.11 | P.Hynes
-4495 | M | train-clean-360  | 25.09 | Dillon Stiles
-4507 | F | test-clean       | 8.05  | Rachel Nelson-Smith
-4511 | F | train-other-500  | 25.81 | Jeanie
-4513 | F | train-other-500  | 7.64  | Gabi
-4515 | M | dev-other        | 10.00 | Doug
-4519 | F | train-clean-360  | 22.04 | Mimi Wang
-4520 | F | train-other-500  | 19.56 | Dorlene Kaplan
-4535 | M | train-clean-360  | 24.62 | Brett W. Downey
-4545 | M | train-other-500  | 30.01 | SunshinePaul
-4546 | F | train-other-500  | 30.18 | Estelle Jobson
-4549 | F | train-other-500  | 26.71 | Kristen Zaza
-4563 | F | train-other-500  | 28.02 | Wiebke
-4570 | M | dev-other        | 10.10 | Bill Mosley
-4572 | M | dev-other        | 10.28 | om123
-4576 | F | train-other-500  | 30.22 | Snaefaxi
-4583 | M | train-other-500  | 27.07 | Jersey City Frankie
-4586 | M | train-clean-360  | 25.05 | Chris Caron
-4590 | M | train-clean-360  | 25.22 | Diapadion
-4591 | M | train-other-500  | 23.65 | Robert White
-4592 | F | train-clean-360  | 25.08 | WestWestest
-4594 | M | train-other-500  | 28.76 | mailman61953
-4595 | M | train-clean-360  | 25.13 | Eric Leach
-4598 | F | train-clean-360  | 21.75 | cher0520
-4599 | M | train-other-500  | 15.72 | xibu
-4629 | M | train-clean-360  | 25.21 | Edward W. LaBonte
-4640 | F | train-clean-100  | 25.06 | Karen Mason
-4652 | F | train-other-500  | 9.07  | Savanna Herrold
-4659 | M | train-other-500  | 30.00 | Stephen Marsh
-4660 | F | train-other-500  | 30.12 | SusieSA
-4667 | F | train-other-500  | 28.29 | Maria Therese
-4680 | F | train-clean-100  | 25.15 | pachayes
-4681 | M | train-clean-360  | 19.11 | Frank Adams
-4687 | M | train-other-500  | 30.13 | mevans
-4693 | M | train-other-500  | 30.25 | Robert Parker
-4697 | F | train-other-500  | 28.58 | Jannie Meisberger
-4699 | M | train-other-500  | 10.43 | Jason Justice
-4701 | M | train-other-500  | 30.11 | CC
-4703 | F | train-other-500  | 24.21 | HurstPP
-4705 | M | train-other-500  | 30.06 | Algy Pug
-4706 | F | train-other-500  | 28.48 | SallyMc
-4710 | M | train-other-500  | 30.18 | David Huston
-4712 | F | train-other-500  | 26.18 | Alisson Veldhuis
-4719 | M | train-clean-360  | 22.99 | M. Craun
-4731 | F | train-clean-360  | 25.27 | Becky Cook
-4733 | M | train-clean-360  | 19.08 | dwegowy
-4734 | M | train-clean-360  | 17.07 | Adib Masumian
-4738 | M | train-other-500  | 29.33 | Leonard Wilson
-4741 | M | train-other-500  | 22.64 | Nullifidian
-4742 | F | train-other-500  | 11.24 | Little Tee
-4744 | F | train-clean-360  | 25.15 | Amy Gramour
-4748 | M | train-other-500  | 30.15 | Dirk Eichhorn
-4750 | M | train-other-500  | 30.12 | Paul Huckerby
-4757 | F | train-other-500  | 10.61 | Grace Dobson
-4766 | F | train-other-500  | 19.73 | Melanie
-4767 | M | train-other-500  | 19.26 | Delysid
-4770 | F | train-clean-360  | 20.50 | Jeanne Luft
-4771 | M | train-other-500  | 21.91 | scrawl
-4773 | F | train-other-500  | 30.04 | Kathryn Lois
-4779 | F | train-other-500  | 30.00 | Angel5
-4788 | M | train-clean-100  | 25.04 | Bill Boerst
-4791 | F | train-other-500  | 30.10 | MelanieMae
-4799 | M | train-other-500  | 29.45 | Matt Judd
-4800 | F | train-clean-360  | 25.14 | Mary Herndon Bell
-4806 | M | train-clean-360  | 16.25 | Jason Ingolfsland
-4807 | F | train-clean-360  | 16.73 | Fuzz
-4813 | M | train-clean-100  | 25.12 | Steve Mattern
-4821 | M | train-other-500  | 17.35 | Zachary Johnson
-4824 | F | train-other-500  | 30.14 | meyerli
-4830 | M | train-clean-100  | 24.05 | George Aalto
-4831 | F | dev-other        | 10.07 | Lisa Meyers
-4836 | M | train-other-500  | 23.06 | Preston Scrape
-4837 | F | train-clean-360  | 21.92 | Sharon Kilmer
-4839 | F | train-clean-360  | 25.06 | Julie K. Rose
-4841 | F | train-other-500  | 26.21 | Haley Pereira
-4846 | F | train-clean-360  | 19.94 | Kathrin Salazar
-4848 | M | train-clean-360  | 25.09 | Dee Wykoff
-4852 | M | test-other       | 10.20 | Arthur Piantadosi
-4853 | F | train-clean-100  | 25.04 | Barbara Derksen
-4854 | M | train-clean-360  | 25.20 | Doctor_wu
-4856 | M | train-clean-360  | 25.04 | Steven Seitel
-4859 | M | train-clean-100  | 20.37 | nathank
-4860 | M | train-clean-360  | 20.58 | Jonah Cummings
-4863 | M | train-other-500  | 30.16 | I M CLIFFORD
-4872 | M | train-other-500  | 30.02 | R E Faust
-4894 | M | train-other-500  | 20.97 | Gary Dzierlenga
-4898 | M | train-clean-100  | 25.11 | greatbasinrain
-4899 | F | train-clean-360  | 25.23 | Theresa L. Downey
-4910 | F | train-other-500  | 21.56 | Amanda Martin Sandino
-4915 | M | train-other-500  | 26.95 | Andrew Bowles
-4926 | F | train-clean-360  | 25.21 | browneyedgirl32382
-4930 | M | train-other-500  | 30.19 | Adrian Levitsky
-4931 | M | train-other-500  | 18.71 | KevS
-4936 | F | train-other-500  | 28.12 | jedopi
-4945 | M | train-clean-360  | 25.07 | Charles RUHE
-4948 | M | train-other-500  | 30.11 | Tom Barron
-4955 | M | train-other-500  | 24.90 | Elwood Mott
-4957 | F | train-clean-360  | 25.06 | P Moscato
-4958 | M | train-other-500  | 16.99 | David P. Sroka
-4959 | F | train-other-500  | 21.22 | Piper Hayes
-4964 | F | train-other-500  | 25.18 | rashada
-4965 | F | train-other-500  | 27.43 | Rachel Craig
-4967 | F | train-clean-360  | 25.02 | Karen
-4969 | M | train-other-500  | 30.07 | Garth Burton
-4970 | F | test-clean       | 8.15  | airandwaters
-4973 | M | train-clean-360  | 25.03 | Michael Lipschultz
-4979 | M | train-other-500  | 22.72 | Eric
-4991 | F | train-other-500  | 28.58 | Jacqueline (Jacqui) Grady
-4992 | F | test-clean       | 8.21  | Joyce Martin
-4993 | M | train-other-500  | 30.22 | BillMac
-5000 | F | train-other-500  | 20.78 | Anna-Lisa Ott
-5002 | M | train-clean-360  | 22.52 | Brendan Stallard
-5005 | F | train-other-500  | 29.27 | pinhsien
-5007 | F | train-clean-360  | 22.98 | Kathleen Nelson
-5009 | M | train-other-500  | 30.12 | Michael Reuss
-5012 | F | train-clean-360  | 25.06 | Morgan Schlicker
-5013 | M | train-other-500  | 25.43 | Joseph Dsouza
-5019 | F | train-other-500  | 29.72 | Sandra Estenson
-5022 | F | train-clean-100  | 25.12 | Kathleen Costa
-5023 | F | train-other-500  | 26.64 | Elizabeth Zaranka
-5029 | F | train-clean-360  | 25.02 | Courtney Sandhu
-5036 | F | train-other-500  | 27.42 | NicolaRuth
-5038 | M | train-other-500  | 30.14 | John Kooz
-5039 | F | train-clean-360  | 25.10 | Inga Parsons
-5043 | M | train-other-500  | 30.25 | edbucks
-5044 | F | train-other-500  | 22.47 | Andrea Keene
-5045 | F | train-other-500  | 30.24 | Mounica
-5049 | M | train-clean-100  | 25.08 | Bradley Smith
-5054 | F | train-clean-360  | 25.13 | Denice Stradling
-5060 | F | train-other-500  | 30.03 | Linda Moreau
-5062 | F | train-clean-360  | 25.09 | E Ogston
-5063 | F | train-clean-360  | 7.96  | susanfrom
-5076 | M | train-other-500  | 30.19 | Jules Hawryluk
-5077 | F | train-other-500  | 30.05 | Sandra G
-5082 | F | train-other-500  | 30.11 | SummerWind
-5092 | M | train-clean-360  | 25.24 | Josh Smith
-5093 | F | train-clean-360  | 25.23 | Nicole Kay
-5101 | F | train-other-500  | 23.83 | E.Lee
-5104 | M | train-clean-100  | 25.03 | Chuck Burke
-5105 | M | test-clean       | 8.12  | elongman
-5115 | F | train-clean-360  | 25.01 | Terry Goodyer
-5118 | M | train-other-500  | 30.13 | klbonds
-5123 | M | train-clean-360  | 25.11 | wvthcomp
-5126 | M | train-clean-360  | 25.06 | Tom Lennon
-5132 | M | train-other-500  | 25.62 | David Stryker
-5133 | F | train-clean-360  | 25.04 | Jo Karabasz
-5136 | F | train-other-500  | 30.21 | Felicity C
-5139 | F | train-clean-360  | 25.16 | Katine
-5141 | M | train-other-500  | 30.17 | David Goldfarb
-5142 | F | test-clean       | 8.07  | Mary Ballard-Johansson
-5147 | M | train-clean-360  | 25.04 | Cody2
-5152 | M | train-other-500  | 30.02 | Liam Neely
-5154 | F | train-clean-360  | 22.28 | Danarra
-5157 | M | train-clean-360  | 25.25 | drewmac
-5163 | F | train-clean-100  | 23.57 | LilyAnne
-5164 | F | train-other-500  | 30.09 | Vsilverlining
-5172 | M | train-other-500  | 15.94 | Equilibrium33
-5181 | M | train-other-500  | 28.26 | Joel Nisbet
-5183 | M | train-other-500  | 30.09 | Kenneth Sergeant Gaghan
-5185 | M | train-other-500  | 27.52 | okei
-5186 | M | train-clean-360  | 25.00 | Brendan Brown
-5189 | M | train-clean-360  | 25.25 | Samanem
-5190 | M | train-clean-360  | 25.15 | Rom Maczka
-5192 | M | train-clean-100  | 24.14 | Jason Esteves
-5198 | M | train-other-500  | 25.89 | Brian
-5199 | F | train-other-500  | 21.67 | Diana Fast
-5206 | M | train-clean-360  | 25.07 | Simon Dexter
-5217 | F | train-other-500  | 4.25  | Anqi Wang
-5220 | F | train-other-500  | 30.03 | Mary Schneider
-5224 | M | train-other-500  | 29.58 | Matt Soar
-5230 | F | train-other-500  | 26.89 | Aubrey Anne
-5233 | F | train-other-500  | 17.95 | summerdaze
-5239 | M | train-clean-360  | 25.05 | compozr
-5242 | M | train-clean-360  | 22.52 | Michael Monhollon
-5244 | F | train-other-500  | 29.49 | Carolin Ksr
-5245 | M | train-other-500  | 26.36 | Dymmesdale
-5246 | M | train-clean-360  | 22.77 | Mike Bloomfield
-5248 | M | train-other-500  | 30.23 | Don Stirno
-5252 | M | train-other-500  | 30.14 | Michael Deng
-5261 | M | train-clean-360  | 25.14 | Ali Kazerani
-5266 | F | train-clean-360  | 23.87 | Jill
-5269 | F | train-other-500  | 4.84  | Elizabeth Barr
-5271 | F | train-other-500  | 24.92 | Amanda
-5278 | M | train-other-500  | 29.66 | Mike Harris
-5280 | M | train-other-500  | 30.05 | penboy7000
-5285 | F | train-other-500  | 30.07 | Sweetlilbirdy
-5287 | F | train-other-500  | 23.96 | adsum iam
-5290 | F | train-clean-360  | 25.04 | Natalie
-5293 | M | train-clean-360  | 21.97 | Ned Troxel
-5296 | F | train-other-500  | 22.97 | Joselyn Hasty
-5299 | F | train-other-500  | 28.69 | Becky Doughty
-5304 | M | train-clean-360  | 21.08 | Don W. Jenkins
-5319 | M | train-clean-360  | 25.13 | Guero
-5321 | M | train-other-500  | 29.97 | Adam Whybray
-5322 | M | train-clean-100  | 25.03 | Jay Bidal
-5325 | M | train-other-500  | 18.83 | Rory Lawton
-5328 | M | train-other-500  | 28.14 | Nathan Jordan
-5333 | M | train-clean-360  | 25.07 | Robert Fletcher
-5337 | F | train-clean-360  | 23.43 | Danielle
-5338 | F | dev-clean        | 8.07  | S R Colon
-5339 | F | train-clean-100  | 25.13 | Lauren McCullough
-5340 | F | train-other-500  | 24.25 | Mary-Beth Blackburn
-5350 | F | train-other-500  | 29.53 | Cath Garde
-5355 | F | train-other-500  | 30.17 | Carrie Heyes
-5361 | F | train-other-500  | 28.03 | scarlettraces
-5375 | F | train-other-500  | 28.35 | BumbleVee
-5379 | M | train-other-500  | 24.72 | lennich
-5386 | M | train-clean-360  | 25.16 | Tim Ferreira
-5389 | M | train-clean-360  | 25.03 | Joseph Lawler
-5390 | M | train-clean-100  | 25.20 | Charles Bice
-5393 | F | train-clean-100  | 25.22 | Amy Hengst
-5400 | F | train-clean-360  | 25.25 | Natalie Sullivan
-5401 | M | train-clean-360  | 25.20 | Andrew Nelson
-5405 | F | train-other-500  | 30.08 | Anka
-5412 | F | train-other-500  | 12.44 | tommybascue
-5424 | F | train-other-500  | 30.04 | Julia Niedermaier
-5429 | M | train-other-500  | 25.06 | Dennis Lane Pretoria
-5439 | F | train-other-500  | 30.06 | Laine S.
-5442 | F | test-other       | 10.11 | oneiros81
-5445 | F | train-other-500  | 30.07 | Liz DeLassus
-5448 | M | train-clean-360  | 20.40 | Bryan Reid
-5456 | M | train-clean-100  | 19.77 | e_scarab
-5459 | M | train-other-500  | 16.04 | bryan.peterson
-5460 | M | train-other-500  | 28.84 | Max Lindberg
-5463 | M | train-clean-100  | 25.00 | GLM
-5468 | F | train-other-500  | 30.12 | ashleighjane
-5471 | F | train-other-500  | 27.22 | ElleyKat
-5480 | F | train-other-500  | 30.01 | ESFJ Girl
-5484 | F | test-other       | 10.11 | Nastassia
-5487 | F | train-other-500  | 28.40 | Michaela O'Connor
-5489 | F | train-clean-360  | 22.21 | LizMourant
-5506 | M | train-other-500  | 29.80 | Frank Booker
-5513 | M | train-clean-360  | 23.93 | David Callahan
-5514 | F | train-clean-100  | 17.39 | Ella Jane Quentin
-5519 | M | train-clean-360  | 23.06 | scoutman77
-5536 | M | dev-clean        | 8.13  | David Mix
-5538 | F | train-clean-360  | 25.06 | Linda Velwest
-5543 | F | dev-other        | 10.07 | Anya
-5545 | M | train-other-500  | 30.04 | Gary Olman
-5561 | F | train-clean-100  | 23.86 | Ellen Jones
-5565 | M | train-other-500  | 17.18 | Ken Padgitt
-5567 | M | train-other-500  | 17.50 | BStapley
-5569 | F | train-other-500  | 17.39 | Beth Hitesman
-5570 | F | train-clean-360  | 25.05 | Corinna Schultz
-5583 | F | train-clean-360  | 25.25 | Claudia Wilson
-5588 | F | train-clean-360  | 25.09 | Gail Timmerman Vaughan
-5604 | F | train-clean-360  | 25.05 | kittyandcheese
-5606 | F | train-clean-360  | 25.11 | PrincessG
-5618 | F | train-clean-360  | 25.07 | Shirleyroses
-5620 | M | train-other-500  | 16.40 | jerryB
-5622 | F | train-clean-360  | 25.15 | Roseanne Schmidt
-5628 | M | train-other-500  | 30.02 | Adrian Wheal
-5635 | M | train-clean-360  | 25.07 | Matthew Reece
-5636 | F | train-other-500  | 30.17 | Joyce Couch
-5637 | M | train-clean-360  | 17.23 | Anthony
-5639 | M | test-clean       | 8.28  | Ulf Bjorklund
-5641 | M | train-other-500  | 27.47 | Gerald Peter Morgan
-5649 | F | train-other-500  | 30.08 | hazelra
-5652 | F | train-clean-100  | 25.05 | amicrazy2u
-5653 | F | train-other-500  | 30.06 | Kimberly Anderson
-5655 | F | train-clean-360  | 25.14 | Christine Rodriguez
-5656 | M | train-clean-360  | 25.17 | Joe Mabry
-5660 | F | train-clean-360  | 25.17 | hollz
-5661 | F | train-other-500  | 30.04 | Dianne Lanning
-5665 | F | train-other-500  | 26.45 | Lynda Sizemore
-5671 | F | train-other-500  | 30.11 | Trihypoo
-5672 | M | train-clean-360  | 25.21 | Jacob Paul Starr
-5678 | M | train-clean-100  | 25.10 | jgoffena
-5682 | F | train-other-500  | 30.15 | Lmwong
-5683 | F | test-clean       | 8.01  | Rachael Lapidis
-5684 | F | train-clean-360  | 22.39 | Elsa Youngsteadt
-5688 | F | train-clean-100  | 25.17 | Jennifer Dionne
-5694 | M | dev-clean        | 8.01  | Winston Tharp
-5700 | M | train-other-500  | 30.13 | Paul Adams
-5703 | M | train-clean-100  | 25.10 | Garth Comira
-5712 | F | train-clean-360  | 25.06 | Alexandra Huckabay
-5717 | M | train-clean-360  | 25.14 | Phil Chenevert
-5719 | M | train-other-500  | 27.22 | John Fricker
-5720 | F | train-other-500  | 30.12 | Thelma Meyer
-5723 | F | train-clean-360  | 25.24 | Linda Hogan
-5724 | F | train-clean-360  | 25.01 | paintgirl
-5725 | F | train-other-500  | 26.86 | doublemirrors
-5727 | M | train-clean-360  | 25.25 | JDavidMoore
-5731 | F | train-clean-360  | 25.14 | Andrea Boltz
-5733 | F | train-other-500  | 30.22 | Spinhop
-5735 | F | train-other-500  | 11.45 | Filipa
-5740 | F | train-clean-360  | 25.22 | Alisa
-5746 | M | train-clean-360  | 25.08 | DL Pead
-5750 | M | train-clean-100  | 25.08 | laurencetrask
-5756 | F | train-other-500  | 25.36 | Mich_elle
-5764 | M | test-other       | 10.06 | Herman Roskams
-5765 | M | train-other-500  | 30.20 | A. J. Carroll
-5767 | M | train-clean-360  | 17.85 | taijohn
-5772 | M | train-other-500  | 30.06 | jessecoy
-5776 | M | train-clean-360  | 17.71 | Chris Pauley
-5778 | F | train-clean-100  | 21.81 | Laura Victoria
-5781 | M | train-other-500  | 30.14 | Parrot17
-5784 | M | train-other-500  | 30.01 | Chris Donnelly
-5789 | F | train-clean-100  | 25.16 | Kirsten Wever
-5791 | M | train-other-500  | 30.15 | croudy
-5796 | F | train-other-500  | 30.15 | Linette Geisel
-5802 | M | train-clean-360  | 25.22 | Christopher Maust
-5808 | M | train-clean-100  | 25.16 | jeandelfrio
-5809 | F | train-clean-360  | 25.25 | kattekliek
-5810 | M | train-clean-360  | 25.02 | Patrick Reinhart
-5825 | F | train-other-500  | 30.25 | Vira Denton
-5826 | M | train-other-500  | 30.12 | Phineas Redux
-5831 | F | train-other-500  | 30.20 | Kirsty Leishman
-5837 | F | train-other-500  | 24.19 | Debbie Pieterse
-5840 | M | train-other-500  | 20.88 | Dick Summerfield
-5849 | F | dev-other        | 10.00 | ashleyspence
-5854 | M | train-other-500  | 21.70 | Gabriel Glenn
-5860 | F | train-other-500  | 27.18 | J L Raimundo
-5867 | F | train-clean-100  | 23.63 | Sharon Omi
-5868 | M | train-clean-360  | 25.06 | alwpoe
-5874 | M | train-other-500  | 29.88 | Paul Andrews
-5876 | F | train-clean-360  | 25.15 | kelleywyskiel
-5883 | M | train-clean-360  | 25.03 | Jerry Romero
-5886 | M | train-other-500  | 20.49 | Simon Brown
-5890 | F | train-other-500  | 16.91 | GabrielleC
-5893 | F | train-other-500  | 30.12 | Elizabeth Fiedler
-5894 | M | train-other-500  | 30.01 | Paul Stephens
-5895 | F | dev-clean        | 8.02  | iamartin
-5906 | F | train-other-500  | 29.04 | Lily-LLM
-5909 | M | train-clean-360  | 25.14 | Mark Mickelson
-5910 | M | train-other-500  | 18.31 | Anthony Lee
-5911 | M | train-other-500  | 30.14 | Paul C. Newman
-5913 | M | train-other-500  | 30.24 | dmbrought
-5914 | M | train-clean-360  | 25.14 | bobbybrill
-5918 | M | train-clean-360  | 25.20 | Mike Wajda
-5929 | F | train-other-500  | 27.51 | Gryphon Perkins
-5933 | M | train-other-500  | 27.99 | Barry O'Neill
-5935 | M | train-clean-360  | 25.07 | Mike Okonek
-5940 | M | train-clean-360  | 25.06 | Gargoyle
-5949 | F | train-other-500  | 28.22 | debolee
-5951 | M | train-other-500  | 25.81 | Stevan Simmons
-5952 | F | train-other-500  | 29.66 | sherlock85
-5968 | F | train-clean-360  | 25.05 | Cate Barratt
-5970 | M | train-other-500  | 27.77 | Larry Degala
-5975 | M | train-clean-360  | 25.12 | Tony Maxey
-5977 | M | train-other-500  | 30.18 | Ken Felt
-5979 | M | train-other-500  | 30.12 | Pentti Hirvonen
-5980 | F | train-other-500  | 17.12 | Nichole Thompson
-5983 | F | train-other-500  | 30.15 | Katew
-5984 | F | train-clean-360  | 25.06 | sbburke
-5985 | M | train-clean-360  | 25.01 | James K. White
-5993 | M | train-other-500  | 30.18 | garyday
-6000 | F | train-clean-100  | 18.04 | MissRose
-6003 | M | train-other-500  | 9.91  | sparks0314
-6006 | F | train-clean-360  | 25.16 | Stephanie Lee
-6009 | M | train-other-500  | 30.01 | AVG
-6010 | M | train-other-500  | 30.17 | Ralph Kerwin
-6014 | F | train-clean-360  | 25.09 | Tina Nuzzi
-6019 | M | train-clean-100  | 25.17 | DerekP
-6025 | F | train-other-500  | 29.77 | Lonelle Yoder
-6030 | F | train-other-500  | 30.00 | Shiromi
-6032 | F | train-clean-360  | 25.14 | Beverly Scott
-6035 | M | train-other-500  | 29.45 | Marty Kris
-6037 | M | train-clean-360  | 8.12  | bish
-6038 | F | train-clean-360  | 25.17 | Sirmelja
-6051 | M | train-other-500  | 24.29 | Ric Cornwall
-6054 | F | train-clean-360  | 18.24 | Leslie Stevens Suhy
-6060 | M | train-clean-360  | 22.81 | Jonathan Lange
-6064 | F | train-clean-100  | 25.04 | Deborah Knight
-6065 | M | train-other-500  | 24.49 | David Olson
-6070 | F | test-other       | 10.01 | Saab
-6072 | F | train-other-500  | 24.62 | CrowGirl
-6075 | M | train-clean-360  | 25.08 | Rob Smith
-6076 | M | train-other-500  | 30.03 | Saethram
-6077 | M | train-other-500  | 30.25 | Steve Belleguelle
-6078 | F | train-clean-100  | 25.02 | dobsonfly
-6080 | M | train-clean-360  | 25.18 | progressingamerica
-6081 | M | train-clean-100  | 25.10 | Lazuli
-6082 | F | train-clean-360  | 25.06 | EyeBones
-6084 | M | train-other-500  | 22.16 | Amallen
-6087 | F | train-other-500  | 27.90 | Caroline Driggs
-6088 | F | train-other-500  | 30.12 | burk
-6097 | M | train-other-500  | 30.12 | Phil Benson
-6098 | M | train-clean-360  | 25.07 | Kancamagus
-6099 | F | train-clean-360  | 25.17 | Cheri Gardner
-6102 | F | train-other-500  | 15.61 | Rachel Bossier
-6104 | F | train-clean-360  | 25.15 | Juliana M.
-6106 | F | train-other-500  | 23.92 | Ruth Kidson
-6111 | F | train-other-500  | 30.14 | Jill Preston
-6115 | M | train-clean-360  | 25.10 | MitchHerd
-6119 | M | train-clean-360  | 18.01 | Vinnie Tesla
-6120 | F | train-clean-360  | 24.31 | Terra Mendoza
-6121 | F | train-other-500  | 30.10 | Imagine
-6123 | F | dev-other        | 10.06 | chocmuse
-6126 | M | train-other-500  | 19.22 | David Abbott
-6127 | F | train-other-500  | 30.20 | Andee
-6128 | F | test-other       | 10.13 | Isabelle Brasme
-6131 | F | train-other-500  | 21.84 | Malane
-6135 | F | train-other-500  | 22.88 | Spike Holcomb
-6138 | F | train-other-500  | 30.09 | Deborah Brabyn
-6139 | F | train-clean-360  | 25.08 | Lois C. Johnson
-6145 | F | train-other-500  | 30.07 | Madeleine Brook
-6147 | F | train-clean-100  | 25.05 | Liberty Stump
-6153 | F | train-other-500  | 30.16 | anjieliu
-6157 | M | train-clean-360  | 22.97 | HotConflict
-6159 | M | train-other-500  | 26.45 | Ted Garvin
-6160 | F | train-clean-360  | 17.76 | FaithR
-6167 | F | train-clean-360  | 25.08 | Janet
-6173 | F | train-other-500  | 30.14 | Carol Box
-6177 | M | train-other-500  | 30.02 | wminbru
-6178 | F | train-other-500  | 21.18 | alegriavida
-6181 | M | train-clean-100  | 25.09 | Mike
-6184 | M | train-other-500  | 23.82 | Adam Tomkins
-6188 | F | train-clean-360  | 22.61 | Roxanna Nazari
-6189 | F | train-clean-360  | 23.67 | Shana Cohen
-6196 | M | train-other-500  | 11.75 | Martin Gradwell
-6199 | M | train-other-500  | 30.08 | bobolink
-6206 | F | train-clean-360  | 25.12 | Yvonne Smith
-6209 | M | train-clean-100  | 25.04 | deckerteach
-6211 | F | train-other-500  | 30.18 | Caeliveres
-6215 | F | train-clean-360  | 25.08 | Janet248
-6221 | M | train-other-500  | 22.84 | pwu909
-6224 | M | train-other-500  | 30.16 | dave k
-6227 | F | train-other-500  | 30.05 | EliMarieHK
-6232 | M | train-other-500  | 18.47 | Paul Denton
-6233 | F | train-clean-360  | 25.24 | Gen Jones
-6235 | F | train-clean-360  | 21.98 | ReadWriteLib
-6236 | M | train-other-500  | 29.03 | jcwyatt
-6241 | M | dev-clean        | 8.05  | badey
-6242 | F | train-other-500  | 26.65 | Lucinda Gainey
-6248 | M | train-other-500  | 30.24 | Kevin Green
-6249 | M | train-other-500  | 20.45 | Thomas A. Copeland
-6251 | F | train-other-500  | 30.03 | Theresa Sheridan
-6254 | F | train-other-500  | 19.26 | Chelsea S.
-6258 | M | train-clean-360  | 20.49 | haggisreflux
-6267 | M | dev-other        | 10.06 | Cata
-6269 | M | train-clean-360  | 25.13 | Don Halpert
-6272 | F | train-clean-100  | 25.14 | jlenardon
-6276 | M | train-other-500  | 30.17 | dan_h
-6281 | F | train-other-500  | 16.33 | Beth Thomas
-6284 | F | train-other-500  | 17.71 | cklee
-6286 | F | train-clean-360  | 25.01 | Wendy Almeida
-6288 | F | train-clean-360  | 25.07 | Sunni West
-6294 | M | train-clean-360  | 24.10 | humanode
-6295 | M | dev-clean        | 8.04  | Michael Packard
-6300 | F | train-clean-360  | 25.07 | Sarah Crampton
-6308 | F | train-clean-360  | 23.36 | Easton
-6311 | M | train-other-500  | 21.53 | TRUEBRIT
-6313 | F | dev-clean        | 8.17  | Jennifer Wiginton
-6317 | F | train-clean-360  | 25.20 | Sarah K
-6319 | F | dev-clean        | 8.01  | thestorygirl
-6323 | M | train-other-500  | 18.48 | Caluminium
-6324 | F | train-other-500  | 7.40  | Barbara E. McCarthy
-6330 | M | train-clean-360  | 25.04 | David Cummings
-6332 | F | train-other-500  | 18.50 | Kelli England
-6333 | M | train-other-500  | 29.43 | Max Korlinge
-6339 | F | train-clean-360  | 25.13 | Michelle Remington
-6341 | F | train-clean-360  | 23.72 | Tiffany J. Kim
-6345 | F | dev-clean        | 8.07  | Jean Bascom
-6346 | M | train-other-500  | 13.65 | Matthew Pagan
-6351 | F | train-other-500  | 24.51 | C. L. W. Rollins
-6352 | F | train-clean-360  | 22.96 | stmacduff
-6353 | F | train-other-500  | 30.20 | Jennifer Randall
-6356 | M | train-other-500  | 30.03 | John O
-6358 | F | train-other-500  | 30.24 | selway
-6359 | F | train-clean-360  | 18.27 | Susan Hanfield
-6364 | M | train-other-500  | 30.02 | Todd Ulbrich
-6367 | M | train-clean-100  | 25.04 | Vince Dee
-6368 | M | train-other-500  | 18.20 | Marc Pizzuti
-6370 | M | train-other-500  | 29.53 | Dan Craig
-6371 | F | train-clean-360  | 21.30 | Rebecca King
-6373 | F | train-clean-360  | 25.20 | Sandie Guenther
-6377 | M | train-other-500  | 30.03 | Maxim Babich
-6378 | F | train-clean-360  | 23.17 | Michelle Day
-6385 | F | train-clean-100  | 25.09 | Novella Serena
-6388 | F | train-clean-360  | 23.04 | Leda
-6391 | F | train-other-500  | 15.17 | polkadotish
-6395 | M | train-clean-360  | 25.06 | Richard Carpenter
-6399 | M | train-other-500  | 29.43 | Edmund Bloxam
-6402 | M | train-other-500  | 29.38 | Chiquito Crasto
-6406 | F | train-clean-360  | 21.92 | Abigail Rasmussen
-6407 | M | train-other-500  | 30.06 | AdamH
-6411 | F | train-other-500  | 18.23 | Feyaza
-6415 | F | train-clean-100  | 25.18 | Daryl Wor
-6418 | M | train-other-500  | 30.02 | Iolo Jones
-6426 | F | train-clean-360  | 25.14 | Pat Redstone
-6432 | M | test-other       | 10.02 | Ditchdog
-6436 | M | train-other-500  | 28.84 | Steve Mattingly
-6437 | M | train-clean-100  | 25.09 | John Hoerr
-6446 | M | train-clean-360  | 25.11 | Bob Gonzalez
-6454 | M | train-clean-100  | 25.22 | David Wales
-6455 | F | dev-other        | 7.14  | Betty Chen
-6458 | M | train-clean-360  | 25.05 | Dennis Blake
-6459 | M | train-other-500  | 26.29 | Nigel Boydell
-6467 | M | dev-other        | 10.04 | sid
-6476 | F | train-clean-100  | 25.17 | Viridian
-6482 | M | train-other-500  | 27.01 | Delmar H Dolbier
-6484 | F | train-other-500  | 29.16 | ilianthe
-6488 | F | train-other-500  | 19.83 | Kendall Ashyby
-6492 | M | train-clean-360  | 22.75 | Hugh Gillis
-6494 | M | train-clean-360  | 25.15 | Morey Kunin
-6497 | M | train-clean-360  | 25.04 | James E. Carson
-6499 | M | train-clean-360  | 25.19 | dave7
-6505 | F | train-clean-360  | 25.00 | Elena
-6506 | M | train-other-500  | 23.36 | Tim Quinn
-6509 | F | train-clean-360  | 25.22 | Marianna Foos
-6510 | M | train-clean-360  | 25.05 | JimD
-6512 | M | train-other-500  | 30.26 | mcgovern1934
-6513 | F | train-other-500  | 30.12 | Susan de Raadt
-6518 | F | train-clean-360  | 25.05 | Wayfarer
-6519 | F | train-clean-360  | 25.23 | Monica Knuppe
-6529 | M | train-clean-100  | 25.04 | Fred DeBerardinis
-6531 | F | train-clean-100  | 21.46 | janesandberg
-6533 | M | train-other-500  | 26.88 | drewmore
-6534 | F | train-other-500  | 30.00 | Helen Falconer
-6535 | M | train-other-500  | 13.21 | Ron Altman
-6538 | M | train-clean-360  | 25.19 | Juan Federico
-6539 | M | train-other-500  | 29.19 | David W. Wolfe
-6540 | M | train-other-500  | 28.77 | Craig Gulliver
-6544 | F | train-clean-360  | 25.22 | Amanda Friday
-6548 | F | train-other-500  | 27.26 | Kristin Gjerlw
-6549 | F | train-other-500  | 28.81 | Sandra Luna
-6550 | M | train-clean-360  | 25.07 | elfpen
-6553 | M | train-clean-360  | 25.18 | DublinGothic
-6555 | M | train-clean-360  | 25.02 | Alexandre Laplante
-6557 | F | train-other-500  | 27.34 | Tika Sabu
-6563 | M | train-clean-100  | 21.58 | William Tomcho
-6567 | F | train-clean-360  | 25.05 | vikvenom
-6568 | F | train-other-500  | 28.93 | Heather Hamtil
-6574 | M | train-clean-360  | 25.02 | Caden Vaughn Clegg
-6575 | M | train-clean-360  | 24.07 | Patrick Painter
-6583 | F | train-other-500  | 30.12 | NastassiaS
-6590 | F | train-other-500  | 30.01 | Marea Brook
-6594 | F | train-other-500  | 30.08 | Amelia Chantarotwong
-6599 | M | dev-other        | 3.03  | rohde
-6609 | M | train-other-500  | 29.53 | Patrick Wallace
-6610 | F | train-other-500  | 30.07 | sylly
-6614 | F | train-other-500  | 28.25 | Veronica Schlette
-6620 | F | train-clean-360  | 12.88 | Amy Koenig
-6625 | M | train-other-500  | 30.10 | hearmeout7
-6627 | F | train-other-500  | 14.57 | Amber Hamilton
-6636 | M | train-other-500  | 27.50 | Philip Martin
-6637 | F | train-clean-360  | 25.21 | Christine Nendza
-6641 | F | train-other-500  | 29.93 | Julienne
-6643 | F | train-clean-360  | 25.14 | cbooren
-6652 | M | train-other-500  | 25.43 | Brendon Wright
-6660 | F | train-other-500  | 30.19 | Nicole Lee
-6668 | M | train-other-500  | 30.01 | Ecological Humanist
-6670 | M | train-other-500  | 29.92 | Mike Pelton
-6673 | F | train-clean-360  | 20.46 | Jenna Lanman
-6674 | M | train-other-500  | 28.55 | jimmylee
-6676 | F | train-other-500  | 27.67 | Chill28
-6683 | F | train-clean-360  | 25.22 | Julia Kelley
-6685 | M | train-other-500  | 23.76 | Jonathan Drury
-6686 | M | train-clean-360  | 20.63 | Elin
-6687 | M | train-other-500  | 27.69 | KirksVoice
-6689 | F | train-other-500  | 28.36 | Tiffany Halla Colonna
-6690 | M | train-clean-360  | 23.34 | T Michael Burke
-6694 | M | train-clean-360  | 25.02 | Ross Williamson
-6695 | F | train-other-500  | 28.66 | Linda Fredericks
-6696 | F | train-clean-360  | 25.14 | Katryn Wiese
-6701 | M | train-clean-360  | 25.13 | Dayle
-6705 | F | train-other-500  | 30.05 | D. A. Frank
-6707 | M | train-other-500  | 27.06 | Lewis
-6709 | M | train-other-500  | 26.15 | vinphizz
-6713 | M | train-other-500  | 12.85 | stephenreader
-6724 | F | train-other-500  | 30.05 | Kristin Young
-6726 | F | train-other-500  | 30.15 | metgmz
-6727 | M | train-clean-360  | 9.40  | Tony Russell
-6733 | F | train-other-500  | 27.65 | Snapdragon
-6735 | F | train-other-500  | 30.11 | fshort
-6741 | F | train-other-500  | 24.76 | Sharon C.
-6743 | M | train-other-500  | 9.91  | Rick Rodney
-6746 | M | train-other-500  | 25.94 | Robin Skelcey
-6747 | M | train-other-500  | 17.49 | Ryan Lothian
-6749 | F | train-other-500  | 30.12 | MaryA
-6752 | M | train-other-500  | 26.78 | maxvon_d
-6753 | M | train-other-500  | 13.29 | T.E. McHenry
-6754 | M | train-other-500  | 30.04 | ToddHW
-6758 | F | train-other-500  | 12.46 | The Gypsy
-6763 | F | train-clean-360  | 21.20 | Manjit Bains
-6773 | M | train-other-500  | 17.55 | MostafaRazavi
-6777 | M | train-other-500  | 30.23 | Rick Saffery
-6782 | F | train-clean-360  | 23.36 | zcameo
-6784 | M | train-other-500  | 29.98 | SteveBuys
-6788 | F | train-clean-360  | 25.11 | Pamela Krantz
-6792 | M | train-other-500  | 30.24 | montmorency
-6794 | F | train-other-500  | 21.11 | Rachel Moyar
-6798 | M | train-other-500  | 30.04 | Aesthete's Readings
-6804 | M | train-other-500  | 30.19 | Nick Duncan
-6807 | F | train-other-500  | 30.08 | Lisa Caputo
-6818 | F | train-clean-100  | 25.17 | beckyboyd
-6821 | F | train-other-500  | 21.87 | Rholdah
-6828 | F | train-clean-360  | 25.11 | Lori Fuller Chugiak, AK
-6829 | F | test-clean       | 8.24  | LadyBug
-6836 | M | train-clean-100  | 25.01 | John
-6841 | M | dev-other        | 10.06 | A. E. Maroney
-6846 | M | train-other-500  | 18.48 | John Leonard
-6848 | M | train-clean-100  | 25.17 | KarlHenning
-6849 | M | train-other-500  | 26.68 | Dan Raynham
-6853 | F | train-other-500  | 22.04 | J. McKnight
-6865 | F | train-clean-360  | 25.03 | Jing Li
-6875 | M | train-other-500  | 21.09 | Bill Miller
-6877 | M | train-clean-360  | 25.23 | Bear Schacht
-6880 | M | train-clean-100  | 25.10 | Capybara
-6882 | M | train-other-500  | 29.24 | David Isenhower
-6883 | M | train-other-500  | 30.05 | Adam Doughty
-6892 | M | train-other-500  | 30.05 | Piotr Nater
-6895 | F | train-clean-360  | 25.23 | Reeses118
-6902 | F | train-other-500  | 23.36 | Barbara Edelman
-6904 | F | train-clean-360  | 24.57 | Kirsten Nelson
-6906 | F | train-other-500  | 20.67 | Joanna1
-6912 | M | train-other-500  | 30.13 | Richard Beck
-6913 | F | train-other-500  | 12.55 | daisyb
-6914 | F | train-other-500  | 28.92 | Katalina Watt
-6918 | F | train-clean-360  | 25.11 | Marilyn Mack
-6923 | M | train-other-500  | 30.15 | Szindbad
-6924 | F | train-clean-360  | 25.12 | Rapunzelina
-6925 | M | train-clean-100  | 17.07 | Thomas Meaney
-6927 | F | train-clean-360  | 25.00 | Sarika Pawar
-6930 | M | test-clean       | 8.00  | Nolan Fout
-6937 | F | train-clean-360  | 23.29 | DVoice
-6938 | F | test-other       | 2.64  | Simmy
-6943 | F | train-other-500  | 30.21 | Chieko Steely
-6945 | M | train-other-500  | 30.07 | Daniel George
-6947 | F | train-other-500  | 30.22 | Grace
-6950 | F | train-other-500  | 27.30 | elmay
-6951 | F | train-other-500  | 18.65 | redhed3095
-6954 | M | train-other-500  | 30.08 | Paul Richards
-6956 | M | train-clean-360  | 25.10 | DannyHauger
-6962 | M | train-other-500  | 10.53 | mattoscarlomas
-6963 | M | train-other-500  | 23.89 | Kasper
-6965 | M | train-clean-360  | 25.00 | NoelBadrian
-6967 | F | train-other-500  | 30.06 | sganatra81
-6974 | M | train-other-500  | 8.01  | Michael Armenta
-6978 | F | train-other-500  | 30.04 | Debra
-6981 | M | train-clean-360  | 25.09 | nlonghu
-6993 | M | train-clean-360  | 25.24 | Seyed
-7000 | M | train-clean-360  | 24.75 | Kevin Alix
-7001 | F | train-other-500  | 30.09 | Arienne
-7008 | M | train-other-500  | 21.23 | John Trevithick
-7009 | M | train-other-500  | 24.38 | roeg11
-7011 | M | train-clean-360  | 17.59 | Icprice
-7012 | F | train-other-500  | 30.00 | Erin McKelle
-7018 | M | test-other       | 10.24 | FSharp
-7021 | M | test-clean       | 8.14  | Nodo420
-7026 | F | train-other-500  | 23.84 | Michele Eaton
-7030 | M | train-clean-360  | 20.57 | Conrad T
-7046 | M | train-other-500  | 24.11 | Pascal Ramseier
-7051 | M | train-clean-360  | 13.88 | Andrew White
-7055 | M | train-other-500  | 30.03 | gemtwist
-7059 | F | train-clean-100  | 25.02 | Joannemmp
-7061 | M | train-clean-360  | 25.26 | AllenJohns
-7062 | F | train-other-500  | 28.30 | Rebecca Thomas
-7065 | M | train-other-500  | 24.16 | smitaj
-7067 | M | train-clean-100  | 25.17 | Matthew Wall
-7069 | M | train-clean-360  | 25.08 | John Schuurman
-7073 | F | train-other-500  | 24.98 | Jill Janovetz
-7078 | F | train-clean-100  | 25.14 | Mary in Arkansas
-7079 | F | train-other-500  | 29.43 | Chuck Williamson
-7085 | M | train-clean-360  | 25.19 | voicebynatalie
-7090 | M | train-clean-360  | 25.05 | Jon Sindell
-7092 | F | train-other-500  | 30.15 | Lorraine B
-7095 | M | train-clean-360  | 25.15 | Wesseling
-7096 | M | train-other-500  | 17.43 | Gary Iredale
-7097 | F | train-other-500  | 26.23 | novelreader
-7105 | M | test-other       | 10.15 | jennycbnyn
-7107 | M | train-other-500  | 30.09 | titankin77
-7113 | F | train-clean-100  | 25.20 | Sukaina Jaffer
-7117 | F | train-clean-360  | 25.17 | Art Leung
-7120 | F | train-clean-360  | 25.10 | L D Hamilton
-7121 | M | train-other-500  | 27.88 | 384403
-7125 | F | train-other-500  | 18.82 | Peggy
-7126 | M | train-clean-360  | 21.69 | pekein
-7127 | M | test-clean       | 8.32  | Bill Kneeland
-7128 | M | train-clean-360  | 25.01 | morganreads
-7131 | F | train-other-500  | 24.22 | Eden Rea-Hedrick
-7134 | M | train-clean-360  | 25.10 | Steve Jackson
-7135 | F | train-other-500  | 29.21 | Maggie Smallwood
-7138 | F | train-other-500  | 30.08 | CaprishaPage
-7139 | M | train-clean-360  | 25.11 | gabrielv
-7140 | F | train-clean-360  | 23.60 | Deanna Bovee
-7143 | F | train-other-500  | 20.54 | Minni Ang
-7145 | F | train-clean-360  | 21.84 | Lita Ledesma
-7147 | M | train-other-500  | 19.94 | S.Nevets
-7148 | F | train-clean-100  | 25.02 | Vickie Ranz
-7150 | M | train-other-500  | 30.20 | asterix
-7155 | M | train-other-500  | 30.11 | pklipp
-7169 | M | train-clean-360  | 16.01 | Ryan Ransom
-7170 | M | train-other-500  | 25.81 | alanmapstone
-7176 | M | test-clean       | 8.06  | KalenXI
-7177 | F | train-other-500  | 15.43 | shana
-7178 | F | train-clean-100  | 25.11 | J.K. Neely
-7188 | M | train-clean-360  | 25.07 | Marty
-7189 | M | train-other-500  | 29.34 | doonaboon
-7190 | M | train-clean-100  | 25.22 | Tony Posante
-7197 | F | train-other-500  | 23.88 | Christine Richardson
-7198 | F | train-other-500  | 27.75 | Daniela Austin
-7199 | F | train-other-500  | 30.01 | T.K. Kirven
-7205 | F | train-other-500  | 30.24 | genierose
-7208 | M | train-other-500  | 30.10 | KK
-7215 | M | train-other-500  | 28.97 | BensonBrunswin
-7218 | F | train-other-500  | 28.76 | MJ Franck
-7220 | F | train-other-500  | 28.59 | LynnAlison
-7223 | F | train-other-500  | 23.26 | eye hear voices
-7226 | M | train-clean-100  | 25.05 | Jonathan Moore
-7228 | M | train-other-500  | 29.36 | Peter John Keeble
-7229 | F | train-clean-360  | 21.53 | Linda Ciano
-7238 | M | train-other-500  | 27.44 | Uday Sagar
-7239 | M | train-other-500  | 19.74 | manofwealth
-7240 | F | train-clean-360  | 25.11 | Lucretia B.
-7241 | M | train-clean-360  | 25.16 | AdrianBisson
-7242 | F | train-other-500  | 30.02 | Renate
-7245 | F | train-clean-360  | 25.04 | Laura Atkinson
-7246 | F | train-other-500  | 27.02 | Lyn Silva
-7247 | M | train-clean-360  | 25.10 | Robert Hoffman
-7250 | F | train-other-500  | 22.08 | A. J. Elliot
-7255 | F | train-other-500  | 18.60 | Jendia
-7258 | M | train-clean-360  | 25.08 | acloward
-7263 | F | train-other-500  | 14.62 | rachaelg
-7264 | M | train-clean-100  | 23.42 | Sean McClain
-7265 | F | train-other-500  | 30.21 | Annie
-7276 | F | train-clean-360  | 25.00 | ASPotter
-7277 | F | train-other-500  | 30.02 | Jenny Bradshaw
-7278 | M | train-clean-100  | 25.01 | Jon Smith
-7285 | F | train-clean-360  | 25.07 | Christina M. Glavas
-7286 | F | train-clean-360  | 23.75 | kindfish
-7294 | F | train-clean-360  | 25.01 | Muriel
-7297 | F | train-clean-360  | 18.65 | Mudlark
-7299 | F | train-other-500  | 21.99 | Barbara Miller
-7301 | M | train-other-500  | 5.54  | jonseverity
-7302 | F | train-clean-100  | 25.10 | Asta1234
-7307 | M | train-other-500  | 27.58 | Anthony Ogus
-7312 | M | train-clean-100  | 5.44  | nkneer
-7313 | M | train-clean-360  | 25.24 | Mark DeVol
-7314 | M | train-clean-360  | 25.17 | Rick Cahill
-7315 | F | train-other-500  | 11.77 | Charlotte Duckett
-7316 | F | train-clean-360  | 18.80 | Joy S Grape
-7318 | F | train-clean-360  | 25.13 | Anise
-7320 | F | train-other-500  | 26.51 | Monika Rolley
-7326 | M | train-other-500  | 20.91 | Adam
-7327 | M | train-other-500  | 25.46 | Tommy Howell
-7331 | F | train-other-500  | 30.15 | AnabelleC
-7333 | F | train-other-500  | 30.22 | Lydia Paterson
-7335 | F | train-clean-360  | 23.72 | Kathryn Louise
-7337 | F | train-other-500  | 24.93 | Adina Owen
-7338 | M | train-other-500  | 30.11 | Eberle Thomas
-7339 | M | train-clean-360  | 25.10 | wildalaska
-7342 | F | train-clean-360  | 25.01 | Carol
-7346 | M | train-other-500  | 17.89 | James Bendall
-7348 | F | train-other-500  | 30.16 | acousticwave
-7354 | M | train-other-500  | 30.01 | TrevorD777
-7357 | M | train-other-500  | 12.31 | rebcult
-7360 | F | train-other-500  | 27.64 | Caroline Hemmerly Kunkle
-7367 | M | train-clean-100  | 25.13 | NIneFive83
-7376 | F | train-other-500  | 30.12 | Nyssa E. Schmidt
-7383 | F | train-clean-360  | 20.20 | Meg Cowan
-7384 | F | train-clean-360  | 20.30 | Diana Dolan
-7387 | F | train-other-500  | 28.58 | Tara Dow
-7389 | M | train-other-500  | 30.17 | Steve C
-7391 | M | train-other-500  | 30.15 | Cary Simz
-7392 | F | train-other-500  | 30.09 | MicheleW
-7395 | F | train-clean-360  | 18.05 | Katherine
-7398 | F | train-clean-360  | 25.09 | Marie Daum
-7402 | M | train-clean-100  | 25.01 | Canby Ibarra
-7408 | M | train-other-500  | 30.14 | David Clarke
-7416 | F | train-clean-360  | 22.16 | Jeanni Hall
-7423 | M | train-other-500  | 30.09 | Gilles G. Le Blanc
-7424 | M | train-other-500  | 30.04 | Christopher Smith
-7433 | F | train-other-500  | 30.00 | Gabriela Cowan
-7434 | F | train-clean-360  | 23.58 | Emily Feuka
-7436 | M | train-other-500  | 24.52 | Gregory Eccles
-7437 | M | train-clean-360  | 25.20 | Graham McMillan
-7445 | F | train-clean-360  | 25.14 | Emily Anderson
-7447 | M | train-clean-100  | 25.24 | dasbury
-7448 | M | train-other-500  | 18.19 | Kevin Johnson
-7460 | M | train-clean-360  | 25.24 | Larry Beasley
-7463 | M | train-other-500  | 30.08 | Pete Milan
-7467 | F | train-other-500  | 29.01 | freshface
-7475 | M | train-clean-360  | 6.10  | Jared Hess
-7478 | M | train-clean-360  | 25.03 | Dan Froese
-7480 | F | train-other-500  | 23.93 | Tricia F.
-7481 | F | train-clean-360  | 22.09 | Karen Howard
-7484 | M | train-clean-360  | 22.63 | Tyran Rexx
-7491 | F | train-other-500  | 30.11 | Lilaread
-7492 | F | train-other-500  | 30.13 | Scheherazade
-7495 | F | train-clean-360  | 25.02 | Arlene Joyce
-7498 | F | train-clean-360  | 20.70 | MarianneVictoria
-7502 | M | train-other-500  | 17.53 | mikefernlea
-7505 | M | train-clean-100  | 25.13 | Ron Lockhart
-7507 | F | train-other-500  | 30.22 | UnaVersal
-7510 | F | train-other-500  | 28.25 | Kathrine Engan
-7511 | F | train-clean-100  | 25.00 | Sherri Vance
-7512 | M | train-other-500  | 15.20 | dudeman
-7514 | F | train-other-500  | 6.57  | Barbara Baker
-7515 | F | train-clean-360  | 25.17 | Cynthia Moyer
-7517 | F | train-clean-100  | 24.29 | Raz Mason
-7518 | M | train-clean-360  | 25.07 | TJDasch
-7520 | M | train-clean-360  | 24.21 | Richard Jackson
-7522 | F | train-other-500  | 15.28 | Kelsey P
-7525 | F | train-clean-360  | 25.19 | Jeannie Tirado
-7538 | M | train-clean-360  | 25.04 | Logan West
-7540 | M | train-clean-360  | 25.26 | Christopher Webber
-7552 | F | train-other-500  | 22.45 | KateC
-7553 | M | train-clean-360  | 25.22 | BDSEmpire
-7555 | F | train-clean-360  | 23.23 | Suebee
-7556 | M | train-other-500  | 30.14 | Alex Lau
-7558 | M | train-clean-360  | 11.00 | J. A. Cook
-7559 | M | train-other-500  | 29.99 | gassumpcao
-7561 | M | train-other-500  | 9.12  | Shawn Bayern
-7565 | M | train-other-500  | 30.07 | Derek
-7569 | M | train-clean-360  | 25.17 | Stephen L. Moss
-7584 | F | train-other-500  | 29.57 | kandice stehlik
-7585 | M | train-other-500  | 30.07 | MaxSitting
-7594 | F | train-clean-360  | 25.00 | Kristel Tretter
-7597 | F | train-other-500  | 30.21 | Inah Derby
-7601 | M | dev-other        | 10.18 | Malone
-7603 | M | train-other-500  | 20.56 | Wupperhippo
-7607 | F | train-other-500  | 7.10  | C F de Rosset
-7608 | M | train-other-500  | 9.33  | rookieblue
-7609 | F | train-other-500  | 30.14 | detroitreads
-7618 | M | train-other-500  | 28.96 | Stipsky
-7635 | F | train-clean-100  | 25.06 | Judy Guinan
-7640 | M | train-other-500  | 28.97 | Dan Darbandi
-7641 | M | dev-other        | 10.03 | Moromis
-7644 | F | train-other-500  | 15.19 | Raphael Platt
-7647 | M | train-clean-360  | 21.83 | Daniel T. Miller
-7649 | F | train-other-500  | 30.16 | Lanerd
-7654 | M | train-other-500  | 12.73 | Word And Mouth
-7657 | F | train-clean-360  | 11.93 | shalclark
-7665 | M | train-clean-360  | 23.27 | Christian Mitchell
-7672 | M | train-other-500  | 30.10 | Cooper Leith
-7679 | F | train-other-500  | 30.14 | Libby Gohn
-7683 | F | train-other-500  | 17.41 | Carolyne
-7687 | F | train-other-500  | 30.05 | Demosthenes
-7688 | M | train-clean-360  | 25.19 | Leslie Walden
-7691 | M | train-other-500  | 26.40 | engineerdst
-7697 | M | dev-other        | 10.07 | JustinJYN
-7699 | F | train-other-500  | 30.11 | DylanTG
-7700 | M | train-other-500  | 28.24 | Dave Wills
-7702 | F | train-other-500  | 16.08 | Linda Jenner
-7704 | M | train-clean-360  | 25.16 | Robert Harder
-7705 | M | train-clean-360  | 25.01 | Timothy Luke
-7708 | F | train-other-500  | 29.71 | Savannah
-7713 | M | train-other-500  | 30.11 | Sacha Chander
-7717 | F | train-clean-360  | 25.01 | Astrid Fingerhut
-7720 | M | train-clean-360  | 25.03 | jeffwiltzius
-7729 | M | test-clean       | 8.17  | Tim Bower
-7730 | F | train-clean-360  | 25.23 | Fiddlesticks
-7732 | M | train-clean-360  | 24.92 | Daniel Vimont
-7733 | F | train-clean-360  | 19.49 | Jewel Raquel
-7737 | F | train-other-500  | 27.17 | Elisabeth Sollog
-7739 | F | train-clean-360  | 25.19 | Melissa Burns-Price
-7746 | M | train-other-500  | 28.01 | Tom Causby
-7749 | M | train-other-500  | 22.83 | Sammy Bean
-7752 | F | train-clean-360  | 25.08 | Samantha Miles
-7754 | F | train-clean-360  | 25.12 | Iridescat
-7756 | F | train-other-500  | 30.12 | Lynne Thompson
-7762 | F | train-other-500  | 17.51 | Fannie
-7764 | M | train-other-500  | 16.55 | Mike Nelson
-7766 | F | train-clean-360  | 25.12 | Maryanka
-7769 | M | train-other-500  | 29.01 | David Biro
-7777 | M | train-clean-360  | 16.44 | Jacob Paine
-7780 | F | train-clean-100  | 23.50 | tazzle
-7783 | F | train-clean-360  | 20.00 | Rebecca Owens
-7786 | M | train-other-500  | 28.11 | Kevin W. Davidson
-7789 | F | train-clean-360  | 25.04 | Lauren Jane
-7794 | F | train-clean-100  | 25.23 | mlcui
-7795 | M | train-other-500  | 18.46 | Cubrick
-7796 | M | train-other-500  | 30.13 | Raybrite
-7800 | F | train-clean-100  | 25.21 | Arie
-7802 | F | train-clean-360  | 25.12 | Samantha J Gubitz
-7809 | M | train-clean-360  | 25.10 | D. T. McGregor
-7816 | F | train-clean-360  | 25.04 | Stefanie Heinrichs
-7823 | M | train-other-500  | 27.40 | Bart in 't Veld
-7825 | M | train-clean-360  | 11.35 | PDyer
-7826 | M | train-other-500  | 27.31 | Dave Shaw
-7828 | M | train-clean-360  | 25.00 | Dan Mason
-7832 | M | train-clean-360  | 25.01 | DJRickyV
-7833 | M | train-clean-360  | 25.15 | Jesse Crisp-Sears
-7835 | M | train-other-500  | 13.79 | kageokami
-7837 | M | train-clean-360  | 22.24 | Alfian
-7839 | F | train-other-500  | 30.22 | Loveday
-7843 | F | train-other-500  | 19.14 | Annette McGuinness
-7848 | M | train-other-500  | 29.06 | bala
-7850 | F | dev-clean        | 8.06  | Jill Engle
-7859 | F | train-clean-100  | 21.58 | xinamarieuhl
-7867 | M | train-clean-360  | 25.08 | nomorejeffs
-7868 | F | train-clean-360  | 25.03 | Chessie Joy
-7871 | M | train-other-500  | 30.15 | Rocit
-7874 | M | train-clean-360  | 25.16 | dgulino
-7879 | F | train-other-500  | 29.10 | deongines
-7881 | M | train-clean-360  | 22.90 | Sam Naishtat
-7883 | F | train-other-500  | 30.03 | Annapurna
-7886 | F | train-other-500  | 7.80  | Mariah Lyons
-7892 | F | train-other-500  | 9.14  | Alexis Castro
-7898 | F | train-other-500  | 30.09 | Coreena
-7902 | M | test-other       | 10.09 | Kyle Van DeGlast
-7909 | M | train-clean-360  | 1.92  | MGreenberg
-7910 | F | train-clean-360  | 25.24 | southernemma
-7912 | M | train-other-500  | 30.19 | Nathan Dickey
-7923 | M | train-other-500  | 26.15 | amaskill
-7925 | F | train-other-500  | 22.64 | Etel Buss
-7926 | M | train-clean-360  | 25.18 | Steven Reynolds
-7932 | F | train-clean-360  | 25.25 | Tammy Stalcup
-7933 | F | train-clean-360  | 25.09 | Highlandoaks
-7938 | M | train-clean-360  | 25.18 | Goss45
-7939 | M | train-clean-360  | 25.19 | DPranitis
-7942 | M | train-other-500  | 30.02 | Luke Sartor
-7945 | F | train-clean-360  | 18.87 | Lois Browne
-7946 | F | train-other-500  | 15.89 | Flash
-7949 | M | train-clean-360  | 22.19 | Jon Miller
-7956 | M | train-clean-360  | 20.78 | Devon Purtz
-7957 | M | train-clean-360  | 21.63 | Wiley Combs
-7959 | F | train-clean-360  | 25.03 | Lily Took
-7962 | F | train-clean-360  | 25.15 | Jolie O'Dell
-7967 | F | train-clean-360  | 22.42 | Lois Hill
-7975 | M | test-other       | 10.04 | William A Crenshaw
-7976 | M | dev-clean        | 8.13  | JenniferRutters
-7981 | M | train-clean-360  | 25.06 | timothyFR
-7982 | F | train-clean-360  | 25.11 | Lori Arsenault
-7988 | M | train-other-500  | 27.62 | JaySands
-7991 | M | train-clean-360  | 25.01 | Aaron Weber
-7994 | F | train-clean-360  | 25.08 | Marian Cervassi
-7995 | F | train-clean-360  | 25.20 | Marie Hoffman
-7997 | M | train-other-500  | 30.16 | Tom Merritt
-8005 | F | train-other-500  | 29.84 | Julia Wells
-8006 | M | train-clean-360  | 25.10 | TyroneS
-8008 | M | train-clean-360  | 25.18 | Paul Adamson
-8009 | F | train-other-500  | 30.10 | Frances Brown
-8011 | M | train-clean-360  | 25.09 | Greg Giordano
-8012 | F | train-other-500  | 30.04 | WoollyBee
-8014 | F | train-clean-100  | 14.42 | constatine
-8015 | F | train-other-500  | 28.07 | Underhill
-8023 | M | train-other-500  | 24.86 | JamesMcAndrew
-8028 | M | train-clean-360  | 25.10 | Tom Geller
-8033 | M | train-other-500  | 30.08 | geoffbl
-8040 | F | train-other-500  | 27.02 | NatalieOram
-8042 | M | train-other-500  | 30.18 | Jack Watson Warr
-8044 | F | train-other-500  | 30.21 | Sarah Hannah
-8050 | M | train-clean-360  | 22.02 | Jake Woldstad
-8051 | F | train-clean-100  | 25.23 | Maria Kasper
-8057 | F | train-clean-360  | 16.93 | Linda Dougherty
-8058 | M | train-other-500  | 19.53 | RLC
-8063 | M | train-clean-100  | 25.08 | Robert Snoza
-8066 | M | train-clean-360  | 25.15 | Tim Cote
-8071 | F | train-other-500  | 30.22 | Vanessa Garcia
-8072 | F | train-other-500  | 8.03  | Kimberly Krause
-8075 | F | train-clean-360  | 25.21 | Melora
-8080 | F | train-clean-360  | 25.23 | e.a.zokaites
-8087 | M | train-other-500  | 30.16 | Arnie Horton
-8088 | M | train-clean-100  | 25.06 | Jason Bolestridge
-8095 | M | train-clean-100  | 25.03 | Theodulf
-8097 | F | train-clean-360  | 25.04 | lewildesen
-8098 | M | train-clean-100  | 25.18 | Arnold
-8108 | M | train-clean-100  | 25.03 | drakaunus
-8112 | F | train-other-500  | 22.66 | Christine Lamberton
-8113 | F | train-clean-360  | 25.06 | PennyAnn
-8118 | F | train-clean-360  | 25.00 | Katie McClain
-8119 | M | train-clean-360  | 25.16 | Malcolm Cameron
-8123 | F | train-clean-100  | 25.15 | Sheila Wood
-8131 | M | test-other       | 10.04 | Christian Alexander
-8138 | M | train-clean-360  | 25.20 | Lee Smalley
-8142 | M | train-clean-360  | 25.06 | Timothy Lucas
-8143 | M | train-other-500  | 30.01 | Nick Whitley
-8148 | F | train-other-500  | 28.08 | Xiph
-8152 | M | train-clean-360  | 25.20 | Bigcowfeet
-8156 | F | train-other-500  | 30.03 | Brooke Cunningham
-8163 | F | train-clean-360  | 25.02 | Greengecko
-8164 | M | train-other-500  | 23.10 | Rob Board
-8168 | F | train-other-500  | 18.41 | Kiera Davidson
-8169 | M | train-other-500  | 25.51 | tovarisch
-8172 | M | train-other-500  | 16.88 | Alan Weyman
-8173 | F | dev-other        | 5.23  | emmablob
-8176 | M | train-clean-360  | 18.78 | Jon Kerfoot
-8180 | F | train-other-500  | 27.70 | Elanor Sakamoto
-8183 | F | train-clean-360  | 25.18 | Cheri Jordan
-8188 | M | test-other       | 10.12 | Matthew Calvin
-8190 | F | train-clean-360  | 21.67 | Lisa Phelps Gonzalez
-8193 | F | train-clean-360  | 25.23 | helengraves
-8194 | F | train-clean-360  | 25.04 | MsMO
-8195 | M | train-clean-360  | 25.04 | Mr Krause
-8197 | F | train-other-500  | 27.75 | Victoria P
-8199 | F | train-other-500  | 26.29 | maryagneskatherine
-8200 | M | train-other-500  | 30.02 | Dan S
-8208 | M | train-other-500  | 30.08 | zaanta
-8215 | M | train-other-500  | 30.23 | readread
-8222 | M | train-clean-360  | 25.09 | Greg Golding
-8224 | M | test-clean       | 8.24  | Leanne Kinkopf
-8225 | M | train-clean-360  | 25.09 | Matt Lusher
-8226 | M | train-clean-100  | 25.18 | Adam Picot
-8228 | F | train-clean-360  | 7.38  | hgal2010
-8230 | M | test-clean       | 8.25  | David Jenkins
-8238 | F | train-clean-100  | 25.08 | Madam Fickle
-8240 | F | train-other-500  | 30.17 | Gertrude Durette
-8242 | F | train-other-500  | 30.13 | Anita Slusser
-8245 | M | train-other-500  | 24.91 | gscheids
-8246 | M | train-other-500  | 18.83 | beeveeo
-8250 | M | train-other-500  | 29.44 | Stephen Gibbons
-8254 | F | dev-other        | 10.05 | Jeana Wei
-8259 | F | train-other-500  | 21.68 | Lawrence
-8262 | M | train-other-500  | 28.44 | Jack Powell
-8266 | M | train-clean-360  | 25.02 | Jeff K.
-8272 | F | train-other-500  | 25.07 | Haili
-8273 | M | train-other-500  | 30.21 | Lucas Boulding
-8280 | F | test-other       | 8.15  | AlaynaMay
-8288 | M | dev-other        | 6.66  | Wayne Donovan
-8291 | M | train-other-500  | 30.19 | Elliot Gage
-8295 | F | train-other-500  | 30.14 | Susan Morin
-8296 | F | train-other-500  | 20.34 | Bria Snow
-8297 | M | dev-clean        | 8.04  | David Mecionis
-8300 | F | train-clean-360  | 24.44 | Judith Parker
-8302 | F | train-other-500  | 25.50 | Charlotte Day
-8307 | M | train-other-500  | 29.32 | Nick Bulka
-8312 | F | train-clean-100  | 25.14 | Jaimie Noy
-8316 | M | train-other-500  | 17.30 | Rocket Rodger
-8321 | F | train-other-500  | 30.15 | SamR
-8322 | M | train-other-500  | 29.61 | yeknod
-8324 | F | train-clean-100  | 25.16 | Kathy Wright
-8328 | M | train-other-500  | 28.24 | William Gavula
-8329 | F | train-clean-360  | 21.14 | Betty Perry
-8334 | M | train-other-500  | 26.14 | Doug Reed
-8337 | F | train-other-500  | 30.09 | Claudia Salto
-8346 | M | train-other-500  | 28.71 | Al Rocca
-8347 | M | train-clean-360  | 21.88 | Terry Torres
-8356 | M | train-other-500  | 30.16 | Paul Mazumdar
-8367 | M | train-other-500  | 24.19 | ophiuroidea
-8382 | F | train-other-500  | 26.80 | Claire Schreuder
-8388 | F | train-clean-360  | 22.87 | Jacki Horn
-8389 | M | train-other-500  | 25.06 | Steven Bateman
-8392 | F | train-other-500  | 23.95 | Katharina Huang
-8394 | M | train-other-500  | 18.93 | Matthew Walker
-8396 | M | train-clean-360  | 25.16 | gloriousjob
-8401 | M | train-clean-360  | 24.29 | Alexander Hatton
-8404 | F | train-clean-360  | 23.61 | Lynne Ray
-8410 | F | train-clean-360  | 25.05 | Shauna Kennett
-8413 | M | train-other-500  | 10.28 | PaulMichael1084
-8414 | F | train-other-500  | 12.07 | jtueller
-8415 | F | train-other-500  | 23.44 | Rusty Dancer
-8419 | M | train-clean-100  | 25.22 | Jon Kissack
-8421 | F | train-clean-360  | 20.02 | Jackie Drown
-8422 | M | train-other-500  | 30.24 | pjhoury
-8424 | F | train-other-500  | 23.87 | Schums
-8425 | M | train-clean-100  | 25.16 | Larry Wilson
-8430 | F | train-other-500  | 30.02 | shihping
-8432 | F | train-other-500  | 10.80 | Emma Joyce
-8441 | F | train-other-500  | 23.64 | ryoko
-8443 | F | train-other-500  | 22.83 | Marsha Payne
-8444 | M | train-other-500  | 29.41 | Anthony Webster
-8445 | M | train-other-500  | 30.21 | Brett G. Hirsch
-8447 | F | train-other-500  | 17.37 | Anastasiia Solokha
-8455 | M | test-clean       | 8.03  | thecheops
-8459 | M | train-clean-360  | 25.09 | sdaeley17
-8461 | F | test-other       | 10.13 | Allie Cingi
-8463 | F | test-clean       | 8.05  | Michele Fry
-8464 | M | train-clean-360  | 18.03 | HappyMiamiDad
-8465 | F | train-clean-100  | 24.88 | TinaNygard2
-8466 | M | train-other-500  | 19.38 | Chris Cartwright
-8468 | F | train-clean-100  | 25.23 | Jennifer Dorr
-8470 | F | train-other-500  | 26.24 | Rebecca Braunert-Plunkett
-8474 | M | train-clean-360  | 25.07 | Scott Snowman
-8476 | F | train-other-500  | 20.74 | Julie Mansius
-8479 | M | train-clean-360  | 14.02 | R.W. Rushing
-8490 | M | train-clean-360  | 16.96 | Matt Parker
-8494 | M | train-clean-360  | 24.20 | Bill Yallalee
-8498 | M | train-clean-360  | 25.10 | Ian Quinlan
-8499 | M | train-other-500  | 30.02 | ReadAllDay
-8500 | M | train-other-500  | 30.12 | Ravi Shankar
-8506 | F | train-clean-360  | 25.03 | Denise Nordell
-8527 | M | train-clean-360  | 25.15 | Gary Bohannon
-8531 | M | train-other-500  | 19.27 | imenadel
-8534 | M | train-clean-360  | 23.94 | Chris Clark
-8536 | M | train-other-500  | 29.74 | Jonathan Brubaker
-8543 | F | train-other-500  | 25.05 | luckyemma
-8544 | F | train-other-500  | 19.21 | Pooja DSr
-8545 | F | train-clean-360  | 25.14 | Joanne Rochon
-8555 | F | test-clean       | 8.03  | Michelle Goode
-8565 | M | train-other-500  | 28.92 | Patrick Eaton
-8573 | F | train-clean-360  | 25.05 | Paige G
-8575 | M | train-clean-360  | 25.04 | Jeremy Robertson
-8576 | M | train-other-500  | 18.24 | Phil Schempf
-8580 | M | train-clean-100  | 19.82 | Gary Dana
-8587 | M | train-other-500  | 25.08 | Drew Johnson
-8590 | M | train-other-500  | 27.96 | bobrose
-8591 | F | train-clean-360  | 22.02 | Jude Somers
-8592 | M | train-clean-360  | 18.66 | Dylan Posa
-8605 | F | train-clean-360  | 25.06 | Kate Sterner
-8609 | M | train-clean-100  | 25.10 | noblesavage
-8619 | M | train-clean-360  | 25.03 | Tad E.
-8625 | F | train-other-500  | 30.16 | Ellen Preckel
-8629 | M | train-clean-100  | 25.13 | Shivansh Dhar
-8630 | M | train-clean-100  | 23.50 | Eduardo
-8631 | M | train-other-500  | 30.06 | Edward Kirkby
-8632 | M | train-other-500  | 21.05 | Geremia
-8635 | M | train-clean-360  | 25.07 | ACBowgus
-8643 | M | train-clean-360  | 25.08 | jabc1950
-8644 | F | train-other-500  | 24.78 | Eenae
-8664 | F | train-other-500  | 30.20 | Pam Castille
-8666 | M | train-other-500  | 25.61 | josembi
-8671 | M | train-other-500  | 16.80 | Simon Smoke
-8675 | M | train-other-500  | 30.11 | Larry Greene
-8677 | F | train-clean-360  | 25.17 | KHand
-8678 | F | train-other-500  | 19.40 | Amy
-8684 | F | train-clean-360  | 21.46 | Alison Stewart
-8687 | M | train-clean-360  | 21.21 | EccentricOwl
-8699 | F | train-clean-360  | 22.64 | Jessica Atha
-8705 | M | train-clean-360  | 25.00 | dsilber01
-8710 | M | train-other-500  | 28.74 | Ralph Crown
-8713 | M | train-clean-360  | 25.23 | Expatriate
-8718 | F | train-clean-360  | 25.07 | Deena Rhoads
-8722 | F | train-clean-360  | 24.13 | Emily Maynard
-8725 | M | train-clean-360  | 25.02 | Gary Ericson
-8742 | M | train-clean-360  | 25.03 | Adam Taylor
-8747 | M | train-clean-100  | 23.49 | DeanOBuchanan
-8753 | M | train-other-500  | 28.87 | Walt Allan
-8758 | M | train-clean-360  | 25.05 | Krzysztof Rowinski
-8765 | M | train-other-500  | 28.03 | jciesielski
-8770 | M | train-clean-100  | 25.13 | Paul Simonin
-8771 | F | train-clean-360  | 15.40 | Anna Millard
-8772 | M | train-clean-360  | 25.03 | Martin Reyto
-8776 | F | train-clean-360  | 21.90 | Taliesin
-8778 | F | train-other-500  | 30.25 | Francoise
-8786 | M | train-clean-360  | 25.12 | Bruce Kachuk
-8791 | M | train-clean-360  | 23.93 | ScottReyonoldsVoice
-8797 | M | train-clean-100  | 22.76 | Sean Grabosky
-8799 | M | train-other-500  | 29.05 | Peter Tucker
-8803 | M | train-other-500  | 23.76 | Ben Lindsey-Clark
-8808 | M | train-other-500  | 22.91 | davidpr
-8820 | M | train-clean-360  | 25.19 | Ignare
-8824 | M | train-clean-360  | 25.21 | Mark Johnston
-8825 | F | train-clean-360  | 23.93 | Erin Schellhase
-8838 | M | train-clean-100  | 25.06 | Kevin Owens
-8842 | F | dev-clean        | 8.10  | Mary J
-8846 | F | train-other-500  | 30.14 | MariaS
-8848 | M | train-clean-360  | 25.12 | Craig Kenneth Bryant
-8855 | M | train-clean-360  | 25.01 | Eric Metzler
-8867 | F | train-other-500  | 7.58  | sorbet87
-8875 | M | train-clean-360  | 17.77 | David D'Huet
-8879 | M | train-clean-360  | 25.07 | Son of the Exiles
-8887 | M | train-clean-360  | 21.11 | Andrew Hernandez
-8897 | F | train-other-500  | 30.15 | beyondutopia
-8975 | F | train-clean-100  | 25.11 | Daisy Flaim
-9000 | M | train-other-500  | 27.26 | Ramon Escamilla
-9022 | F | train-clean-360  | 25.17 | Claire M
-9023 | F | train-clean-360  | 25.19 | P. J. Morgan
-9026 | F | train-clean-360  | 21.75 | Tammy Porter
diff --git a/speaker/tacotron_mel_e10.pt b/speaker/tacotron_mel_e10.pt
deleted file mode 100644
index 0bae17381f301d4f81bae2321e35409fd94d8fbb..0000000000000000000000000000000000000000
--- a/speaker/tacotron_mel_e10.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9799bc6035aa1e555968c1fb2f1ca8b8bb0cdb10f11875cb4cbc1411d811a59b
-size 5861083
diff --git a/speaker/train.py b/speaker/train.py
deleted file mode 100644
index 89c660f337505d3b84e4973cf472749050bec547..0000000000000000000000000000000000000000
--- a/speaker/train.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import torch
-import torchaudio.datasets as datasets
-import torchaudio.transforms as transforms
-from speaker.data import SpeakerMelLoader
-from speaker.model import SpeakerEncoder
-from speaker.utils import get_mapping_array
-
-from sklearn.manifold import TSNE
-from sklearn.decomposition import PCA
-from sklearn.metrics import silhouette_score
-
-from matplotlib import pyplot as plt
-
-import os
-from os import path
-
-import numpy as np
-
-diagram_path = 'diagrams'
-accuracy_path = 'accuracy'
-loss_path = 'loss'
-silhouette_path = 'silhouette'
-tsne_path = 'tsne'
-
-
-def load_data(directory=".", batch_size=4, format='speaker', utter_per_speaker = 4, mel_type='Tacotron'):
-    dataset = SpeakerMelLoader(datasets.LIBRISPEECH(directory, download=True), format, utter_per_speaker,mel_type=mel_type)
-    return torch.utils.data.DataLoader(
-        dataset,
-        batch_size,
-        num_workers=4,
-        shuffle=True
-    )
-
-
-def load_validation(directory=".", batch_size=4, format='speaker', utter_per_speaker = 4, mel_type='Tacotron'):
-    dataset = SpeakerMelLoader(datasets.LIBRISPEECH(directory, "dev-clean",download=True), format, utter_per_speaker,mel_type=mel_type)
-    return torch.utils.data.DataLoader(
-        dataset,
-        batch_size,
-        num_workers=4,
-        shuffle=True
-    )
-
-
-def train(speaker_per_batch=4, utter_per_speaker=4, epochs=2, learning_rate=1e-4, mel_type='Tacotron'):
-    # Init data loader
-    train_loader = load_data(".", speaker_per_batch, 'speaker', utter_per_speaker,mel_type=mel_type)
-    valid_loader = load_validation(".", speaker_per_batch, 'speaker', utter_per_speaker,mel_type=mel_type)
-
-    # Device
-    # Loss calc may run faster on cpu
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    loss_device = torch.device("cpu")
-
-    # Init model
-    model = SpeakerEncoder(device, loss_device)
-    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-
-    sil_scores = np.zeros(0)
-    gender_scores = np.zeros(0)
-    val_losses = np.zeros(0)
-    val_accuracy = np.zeros(0)
-
-    gender_mapper = get_mapping_array()
-
-    # Train loop
-    for e in range(epochs):
-        print('epoch:', e+1, 'of', epochs)
-
-        model.train()
-        # train_ids = np.zeros(0)
-        # train_embeds = np.zeros((0, 256))
-        for step, batch in enumerate(train_loader):
-            #Forward
-            #inputs: (speaker, utter, mel_len, mel_channel)
-            speaker_id, inputs = batch
-            #embed_inputs: (speaker*utter, mel_len, mel_channel)
-            embed_inputs = inputs.reshape(-1, *(inputs.shape[2:])).to(device)
-            #embeds: (speaker*utter, embed_dim)
-            embeds = model(embed_inputs)
-            #loss_embeds: (speaker, utter, embed_dim)
-            loss_embeds = embeds.view((speaker_per_batch,utter_per_speaker,-1)).to(loss_device)
-            loss = model.softmax_loss(loss_embeds)
-
-            if step % 10 == 0:
-                print('train e{}-s{}:'.format(e + 1, step + 1), 'loss', loss)
-
-            #Backward
-            model.zero_grad()
-            loss.backward()
-            model.gradient_clipping()
-            optimizer.step()
-
-            # train_ids = np.concatenate((train_ids, np.repeat(speaker_id, inputs.shape[1])))
-            # train_embeds = np.concatenate((train_embeds, embeds))
-        
-        model.eval()
-        loss = 0
-        acc = 0
-
-        valid_ids = np.zeros(0)
-        valid_embeds = np.zeros((0, 256))
-
-        for step,batch in enumerate(valid_loader):
-            with torch.no_grad():
-                speaker_id, inputs = batch
-                embed_inputs = inputs.reshape(-1, *(inputs.shape[2:])).to(device)
-                embeds = model(embed_inputs)
-                loss_embeds = embeds.view((speaker_per_batch,utter_per_speaker,-1)).to(loss_device)
-                loss += model.softmax_loss(loss_embeds)
-                acc += model.accuracy(loss_embeds)
-                valid_ids = np.concatenate((valid_ids, np.repeat(speaker_id, inputs.shape[1])))
-                valid_embeds = np.concatenate((valid_embeds, embeds.to(loss_device).detach()))
-
-        val_losses = np.concatenate((val_losses, [loss.to(loss_device).detach() / (step + 1)]))
-        val_accuracy = np.concatenate((val_accuracy, [acc.to(loss_device).detach() / (step + 1)]))
-        sil_scores = np.concatenate((sil_scores, [silhouette_score(valid_embeds, valid_ids)]))
-        gender_scores = np.concatenate((gender_scores, [silhouette_score(valid_embeds, gender_mapper[valid_ids.astype('int')])]))
-        print('valid e{}'.format(e + 1), 'loss', val_losses[-1])
-        print('valid e{}'.format(e + 1), 'accuracy', val_accuracy[-1])
-        print('silhouette score', sil_scores[-1])
-        print('gender silhouette score', gender_scores[-1])
-
-        plot_speaker_embeddings(valid_embeds, valid_ids, f'tsne_e{e + 1}_speaker.png', f'T-SNE Plot: Epoch {e + 1}')
-        plot_random_embeddings(valid_embeds, valid_ids, f'tsne_e{e + 1}_random.png', title=f'T-SNE Plot: Epoch {e + 1}')
-        plot_gender_embeddings(valid_embeds, valid_ids, f'tsne_e{e + 1}_gender.png', f'T-SNE Plot: Epoch {e + 1}')
-
-        save_model(model, path.join('speaker', f'saved_model_e{e + 1}.pt'))
-
-        plt.figure()
-        plt.title('Silhouette Scores')
-        plt.xlabel('Epoch')
-        plt.ylabel('Silhouette Score')
-        plt.plot(np.arange(e + 1) + 1, sil_scores)
-        # plt.show()
-        plt.savefig(path.join(diagram_path, silhouette_path, f'sil_scores_{e + 1}.png'))
-        plt.close()
-
-        plt.figure()
-        plt.title('Silhouette Scores over Gender')
-        plt.xlabel('Epoch')
-        plt.ylabel('Silhouette Score')
-        plt.plot(np.arange(e + 1) + 1, gender_scores)
-        # plt.show()
-        plt.savefig(path.join(diagram_path, silhouette_path, f'gender_scores_{e + 1}.png'))
-        plt.close()
-
-        plt.figure()
-        plt.title('Validation Loss')
-        plt.xlabel('Epoch')
-        plt.ylabel('Loss')
-        plt.plot(np.arange(e + 1) + 1, val_losses)
-        # plt.show()
-        plt.savefig(path.join(diagram_path, loss_path, f'val_losses_{e + 1}.png'))
-        plt.close()
-
-        plt.figure()
-        plt.title('Validation Accuracy')
-        plt.xlabel('Epoch')
-        plt.ylabel('Accuracy')
-        plt.plot(np.arange(e + 1) + 1, val_accuracy)
-        # plt.show()
-        plt.savefig(path.join(diagram_path, accuracy_path, f'val_accuracy_{e + 1}.png'))
-        plt.close()
-        
-    return model
-
-
-def save_model(model, path):
-    #Save model state to path
-    torch.save(model.state_dict(),path)
-
-
-def load_model(path, device = None):
-    #Instantiate Model
-    if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    loss_device = torch.device("cpu")
-    model = SpeakerEncoder(device, loss_device)
-
-    #Load model state
-    model.load_state_dict(torch.load(path))
-    # Try this if running on multi-gpu setup or running model on cpu
-    # https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-across-devices
-    # model.load_state_dict(torch.load(PATH, map_location=device))
-    return model
-
-
-def check_model(path):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    loss_device = torch.device("cpu")
-
-    print('**loading model')
-    model = load_model(path)
-
-    print('**loading data')
-    # data = load_data()
-    data = load_validation()
-
-    print('**running model')
-    loss_total = 0
-    acc_total = 0
-    all_ids = np.zeros(0)
-    all_embeds = np.zeros((0, 256))
-
-    for step, batch in enumerate(data):
-        speaker_id, inputs = batch
-
-        print('batch:', step)
-        embed_inputs = inputs.reshape(-1, *(inputs.shape[2:])).to(device)
-        embeds = model(embed_inputs)
-        loss_embeds = embeds.view(*(inputs.shape[:2]),-1).to(loss_device)
-        loss = model.softmax_loss(loss_embeds)
-        accuracy = model.accuracy(loss_embeds)
-
-        all_ids = np.concatenate((all_ids, np.repeat(speaker_id, inputs.shape[1])))
-        all_embeds = np.concatenate((all_embeds, embeds.to(loss_device).detach()))
-
-        loss_total += loss
-        acc_total += accuracy
-        
-        # print('inputs.shape',inputs.shape)
-        # print('embed_inputs.embed_inputs',embeds.shape)
-        # print('embeds.shape',embeds.shape)
-        # print('loss_embeds.shape',loss_embeds.shape)
-        # print('loss.shape',loss.shape)
-        # print('loss',loss)
-        # print('accuracy',accuracy)
-    
-    print('average loss', loss_total / (step+1))
-    print('average accuracy', acc_total / (step+1))
-    print('silhouette score', silhouette_score(all_embeds, all_ids))
-    plot_speaker_embeddings(all_embeds, all_ids, f'tsne_saved_speaker.png', f'T-SNE Plot')
-    plot_random_embeddings(all_embeds, all_ids, f'tsne_saved_random.png', title=f'T-SNE Plot')
-    plot_gender_embeddings(all_embeds, all_ids, f'tsne_saved_gender.png', f'T-SNE Plot')
-
-
-def plot_gender_embeddings(embeddings, ids, filename, title='T-SNE Plot'):
-    # Per https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-    # reducing dimensionality before running TSNE
-    pca = PCA(50)
-    reduction = pca.fit_transform(embeddings)
-    tsne = TSNE(init='pca', learning_rate='auto')
-    transformed = tsne.fit_transform(reduction)
-
-    gender_mapper = get_mapping_array()
-    genders = gender_mapper[ids.astype('int')]
-    females = genders == 1
-    males = genders == 2
-
-    plt.figure()
-    plt.title(title)
-
-    plt.scatter(transformed[females, 0], transformed[females, 1], label='Female')
-    plt.scatter(transformed[males, 0], transformed[males, 1], label='Male')
-    plt.legend()
-    plt.grid()
-    # plt.show()
-    plt.savefig(path.join(diagram_path, tsne_path, filename))
-    plt.close()
-
-
-def plot_speaker_embeddings(embeddings, ids, filename, title='T-SNE Plot'):
-    # Per https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-    # reducing dimensionality before running TSNE
-    pca = PCA(50)
-    reduction = pca.fit_transform(embeddings)
-    tsne = TSNE(init='pca', learning_rate='auto')
-    transformed = tsne.fit_transform(reduction)
-
-    ids = ids.astype('int')
-    unique_ids = np.unique(ids)
-
-    plt.figure()
-    plt.title(f'{title} Speakers')
-
-    for speaker_id in unique_ids:
-        speaker_idx = ids == speaker_id
-        plt.scatter(transformed[speaker_idx, 0], transformed[speaker_idx, 1], label=f'Speaker {speaker_id}')
-
-    # plt.legend()
-    plt.grid()
-    # plt.show()
-    plt.savefig(path.join(diagram_path, tsne_path, filename))
-    plt.close()
-
-
-def plot_random_embeddings(embeddings, ids, filename, size=15, title='T-SNE Plot Random'):
-    # Per https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
-    # reducing dimensionality before running TSNE
-    pca = PCA(50)
-    reduction = pca.fit_transform(embeddings)
-    tsne = TSNE(init='pca', learning_rate='auto')
-    transformed = tsne.fit_transform(reduction)
-
-    ids = ids.astype('int')
-    unique_ids = np.unique(ids)
-    random_unique_ids = np.random.choice(ids, size=min(size, unique_ids.size), replace=False)
-
-    plt.figure()
-
-    plt.title(f'{title} - {random_unique_ids.size} Speakers')
-
-    for speaker_id in random_unique_ids:
-        speaker_idx = ids == speaker_id
-        plt.scatter(transformed[speaker_idx, 0], transformed[speaker_idx, 1], label=f'Speaker {speaker_id}')
-
-    # plt.legend()
-    plt.grid()
-    # plt.show()
-    plt.savefig(path.join(diagram_path, tsne_path, filename))
-    plt.close()
-
-
-if __name__ == '__main__':
-    os.makedirs(diagram_path, exist_ok=True)
-    os.makedirs(path.join(diagram_path, loss_path), exist_ok=True)
-    os.makedirs(path.join(diagram_path, accuracy_path), exist_ok=True)
-    os.makedirs(path.join(diagram_path, tsne_path), exist_ok=True)
-    os.makedirs(path.join(diagram_path, silhouette_path), exist_ok=True)
-    # for speaker_id, mel in load_data():
-    #     print(speaker_id, mel.shape)
-
-    # Might make sense to adjust speaker / utterance per batch, e.g. 64/10    
-    m = train(epochs=300)
-
-    # save_model(m,'speaker/saved_model.pt')
-    check_model('speaker/saved_model_e175.pt')
diff --git a/speaker/utils.py b/speaker/utils.py
deleted file mode 100644
index 0a9624b02c6b3c83bee8f18fc7e6acab28eeeae9..0000000000000000000000000000000000000000
--- a/speaker/utils.py
+++ /dev/null
@@ -1,28 +0,0 @@
-
-import numpy as np
-
-__mapping_array = None
-
-with open('speaker/speakers.txt') as speakers:
-    lines = []
-    for line in speakers.readlines():
-        if line[0] == ';':
-            continue
-        lines.append(line)
-
-    rows = [line.split('|') for line in lines]
-
-    __mapping_list = [(int(row[0].strip()), row[1].strip()) for row in rows]
-
-    max_id = max([speaker_id for (speaker_id, _) in __mapping_list])\
-
-    __mapping_array = np.zeros(max_id + 1,)
-    for speaker_id, gender in __mapping_list:
-        if gender == 'F':
-            __mapping_array[speaker_id] = 1
-        else:
-            __mapping_array[speaker_id] = 2
-
-
-def get_mapping_array():
-    return np.copy(__mapping_array)
diff --git a/speaker_audio/ariana.wav b/speaker_audio/ariana.wav
deleted file mode 100644
index 1c2fe7af8cfc4c830b24542c6eaa79645b3766f7..0000000000000000000000000000000000000000
Binary files a/speaker_audio/ariana.wav and /dev/null differ
diff --git a/speaker_audio/elon_musk.wav b/speaker_audio/elon_musk.wav
deleted file mode 100644
index e306c6aba419042cdf9ab5a580445105d0e888fa..0000000000000000000000000000000000000000
Binary files a/speaker_audio/elon_musk.wav and /dev/null differ
diff --git a/speaker_audio/leviOsa.wav b/speaker_audio/leviOsa.wav
deleted file mode 100644
index 2f0626815f42ea930dd4cc49cf5e92f1b5c30800..0000000000000000000000000000000000000000
Binary files a/speaker_audio/leviOsa.wav and /dev/null differ
diff --git a/speaker_audio/obama.wav b/speaker_audio/obama.wav
deleted file mode 100644
index d772b8adabdd3ccdb3e2878b9a81321303e2b655..0000000000000000000000000000000000000000
Binary files a/speaker_audio/obama.wav and /dev/null differ
diff --git a/stft.py b/stft.py
index 330dac76d3374137e79afad2f65533bf2fd8965e..94709d37d1a81e236bf22e1f51a1efc9e3416ffa 100644
--- a/stft.py
+++ b/stft.py
@@ -38,9 +38,6 @@ from scipy.signal import get_window
 from librosa.util import pad_center, tiny
 from audio_processing import window_sumsquare
 
-use_cuda = torch.cuda.is_available()
-device = torch.device('cuda' if use_cuda else 'cpu')
-
 
 class STFT(torch.nn.Module):
     """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
@@ -67,7 +64,7 @@ class STFT(torch.nn.Module):
             assert(filter_length >= win_length)
             # get window and zero center pad it to filter_length
             fft_window = get_window(window, win_length, fftbins=True)
-            fft_window = pad_center(fft_window, size=filter_length)
+            fft_window = pad_center(fft_window, filter_length)
             fft_window = torch.from_numpy(fft_window).float()
 
             # window the bases
@@ -127,7 +124,7 @@ class STFT(torch.nn.Module):
                 np.where(window_sum > tiny(window_sum))[0])
             window_sum = torch.autograd.Variable(
                 torch.from_numpy(window_sum), requires_grad=False)
-            window_sum = window_sum.to(device) if magnitude.is_cuda else window_sum
+            window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
             inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
 
             # scale by hop ratio
diff --git a/tacotron_mel_e10.pt b/tacotron_mel_e10.pt
deleted file mode 100644
index 0bae17381f301d4f81bae2321e35409fd94d8fbb..0000000000000000000000000000000000000000
--- a/tacotron_mel_e10.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9799bc6035aa1e555968c1fb2f1ca8b8bb0cdb10f11875cb4cbc1411d811a59b
-size 5861083
diff --git a/encoder/__init__.py b/text/banana.py
similarity index 100%
rename from encoder/__init__.py
rename to text/banana.py
diff --git a/train.py b/train.py
index 71412f8316605b9bdd91bff19796c041ae5e01c4..88b09494ce5dfc77e267aacd50c71f22a3ee66f4 100644
--- a/train.py
+++ b/train.py
@@ -1,12 +1,290 @@
-import torch
+import os
+import time
+import argparse
+import math
 from numpy import finfo
 
+import torch
+from distributed import apply_gradient_allreduce
+import torch.distributed as dist
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.data import DataLoader
+
 from model import Tacotron2
+from data_utils import TextMelLoader, TextMelCollate
+from loss_function import Tacotron2Loss
+from logger import Tacotron2Logger
 from hparams import create_hparams
 
+
+def reduce_tensor(tensor, n_gpus):
+    rt = tensor.clone()
+    dist.all_reduce(rt, op=dist.reduce_op.SUM)
+    rt /= n_gpus
+    return rt
+
+
+def init_distributed(hparams, n_gpus, rank, group_name):
+    assert torch.cuda.is_available(), "Distributed mode requires CUDA."
+    print("Initializing Distributed")
+
+    # Set cuda device so everything is done on the right GPU.
+    torch.cuda.set_device(rank % torch.cuda.device_count())
+
+    # Initialize distributed communication
+    dist.init_process_group(
+        backend=hparams.dist_backend, init_method=hparams.dist_url,
+        world_size=n_gpus, rank=rank, group_name=group_name)
+
+    print("Done initializing distributed")
+
+
+def prepare_dataloaders(hparams):
+    # Get data, data loaders and collate function ready
+    trainset = TextMelLoader(hparams.training_files, hparams)
+    valset = TextMelLoader(hparams.validation_files, hparams)
+    collate_fn = TextMelCollate(hparams.n_frames_per_step)
+
+    if hparams.distributed_run:
+        train_sampler = DistributedSampler(trainset)
+        shuffle = False
+    else:
+        train_sampler = None
+        shuffle = True
+
+    train_loader = DataLoader(trainset, num_workers=1, shuffle=shuffle,
+                              sampler=train_sampler,
+                              batch_size=hparams.batch_size, pin_memory=False,
+                              drop_last=True, collate_fn=collate_fn)
+    return train_loader, valset, collate_fn
+
+
+def prepare_directories_and_logger(output_directory, log_directory, rank):
+    if rank == 0:
+        if not os.path.isdir(output_directory):
+            os.makedirs(output_directory)
+            os.chmod(output_directory, 0o775)
+        logger = Tacotron2Logger(os.path.join(output_directory, log_directory))
+    else:
+        logger = None
+    return logger
+
+
 def load_model(hparams):
-    model = Tacotron2(hparams)
+    model = Tacotron2(hparams).cuda()
     if hparams.fp16_run:
         model.decoder.attention_layer.score_mask_value = finfo('float16').min
 
+    if hparams.distributed_run:
+        model = apply_gradient_allreduce(model)
+
+    return model
+
+
+def warm_start_model(checkpoint_path, model, ignore_layers):
+    assert os.path.isfile(checkpoint_path)
+    print("Warm starting model from checkpoint '{}'".format(checkpoint_path))
+    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+    model_dict = checkpoint_dict['state_dict']
+    if len(ignore_layers) > 0:
+        model_dict = {k: v for k, v in model_dict.items()
+                      if k not in ignore_layers}
+        dummy_dict = model.state_dict()
+        dummy_dict.update(model_dict)
+        model_dict = dummy_dict
+    model.load_state_dict(model_dict)
     return model
+
+
+def load_checkpoint(checkpoint_path, model, optimizer):
+    assert os.path.isfile(checkpoint_path)
+    print("Loading checkpoint '{}'".format(checkpoint_path))
+    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+    model.load_state_dict(checkpoint_dict['state_dict'])
+    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+    learning_rate = checkpoint_dict['learning_rate']
+    iteration = checkpoint_dict['iteration']
+    print("Loaded checkpoint '{}' from iteration {}" .format(
+        checkpoint_path, iteration))
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, filepath):
+    print("Saving model and optimizer state at iteration {} to {}".format(
+        iteration, filepath))
+    torch.save({'iteration': iteration,
+                'state_dict': model.state_dict(),
+                'optimizer': optimizer.state_dict(),
+                'learning_rate': learning_rate}, filepath)
+
+
+def validate(model, criterion, valset, iteration, batch_size, n_gpus,
+             collate_fn, logger, distributed_run, rank):
+    """Handles all the validation scoring and printing"""
+    model.eval()
+    with torch.no_grad():
+        val_sampler = DistributedSampler(valset) if distributed_run else None
+        val_loader = DataLoader(valset, sampler=val_sampler, num_workers=1,
+                                shuffle=False, batch_size=batch_size,
+                                pin_memory=False, collate_fn=collate_fn)
+
+        val_loss = 0.0
+        for i, batch in enumerate(val_loader):
+            x, y = model.parse_batch(batch)
+            y_pred = model(x)
+            loss = criterion(y_pred, y)
+            if distributed_run:
+                reduced_val_loss = reduce_tensor(loss.data, n_gpus).item()
+            else:
+                reduced_val_loss = loss.item()
+            val_loss += reduced_val_loss
+        val_loss = val_loss / (i + 1)
+
+    model.train()
+    if rank == 0:
+        print("Validation loss {}: {:9f}  ".format(iteration, val_loss))
+        logger.log_validation(val_loss, model, y, y_pred, iteration)
+
+
+def train(output_directory, log_directory, checkpoint_path, warm_start, n_gpus,
+          rank, group_name, hparams):
+    """Training and validation logging results to tensorboard and stdout
+
+    Params
+    ------
+    output_directory (string): directory to save checkpoints
+    log_directory (string) directory to save tensorboard logs
+    checkpoint_path(string): checkpoint path
+    n_gpus (int): number of gpus
+    rank (int): rank of current gpu
+    hparams (object): comma separated list of "name=value" pairs.
+    """
+    if hparams.distributed_run:
+        init_distributed(hparams, n_gpus, rank, group_name)
+
+    torch.manual_seed(hparams.seed)
+    torch.cuda.manual_seed(hparams.seed)
+
+    model = load_model(hparams)
+    learning_rate = hparams.learning_rate
+    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate,
+                                 weight_decay=hparams.weight_decay)
+
+    if hparams.fp16_run:
+        from apex import amp
+        model, optimizer = amp.initialize(
+            model, optimizer, opt_level='O2')
+
+    if hparams.distributed_run:
+        model = apply_gradient_allreduce(model)
+
+    criterion = Tacotron2Loss()
+
+    logger = prepare_directories_and_logger(
+        output_directory, log_directory, rank)
+
+    train_loader, valset, collate_fn = prepare_dataloaders(hparams)
+
+    # Load checkpoint if one exists
+    iteration = 0
+    epoch_offset = 0
+    if checkpoint_path is not None:
+        if warm_start:
+            model = warm_start_model(
+                checkpoint_path, model, hparams.ignore_layers)
+        else:
+            model, optimizer, _learning_rate, iteration = load_checkpoint(
+                checkpoint_path, model, optimizer)
+            if hparams.use_saved_learning_rate:
+                learning_rate = _learning_rate
+            iteration += 1  # next iteration is iteration + 1
+            epoch_offset = max(0, int(iteration / len(train_loader)))
+
+    model.train()
+    is_overflow = False
+    # ================ MAIN TRAINNIG LOOP! ===================
+    for epoch in range(epoch_offset, hparams.epochs):
+        print("Epoch: {}".format(epoch))
+        for i, batch in enumerate(train_loader):
+            start = time.perf_counter()
+            for param_group in optimizer.param_groups:
+                param_group['lr'] = learning_rate
+
+            model.zero_grad()
+            x, y = model.parse_batch(batch)
+            y_pred = model(x)
+
+            loss = criterion(y_pred, y)
+            if hparams.distributed_run:
+                reduced_loss = reduce_tensor(loss.data, n_gpus).item()
+            else:
+                reduced_loss = loss.item()
+            if hparams.fp16_run:
+                with amp.scale_loss(loss, optimizer) as scaled_loss:
+                    scaled_loss.backward()
+            else:
+                loss.backward()
+
+            if hparams.fp16_run:
+                grad_norm = torch.nn.utils.clip_grad_norm_(
+                    amp.master_params(optimizer), hparams.grad_clip_thresh)
+                is_overflow = math.isnan(grad_norm)
+            else:
+                grad_norm = torch.nn.utils.clip_grad_norm_(
+                    model.parameters(), hparams.grad_clip_thresh)
+
+            optimizer.step()
+
+            if not is_overflow and rank == 0:
+                duration = time.perf_counter() - start
+                print("Train loss {} {:.6f} Grad Norm {:.6f} {:.2f}s/it".format(
+                    iteration, reduced_loss, grad_norm, duration))
+                logger.log_training(
+                    reduced_loss, grad_norm, learning_rate, duration, iteration)
+
+            if not is_overflow and (iteration % hparams.iters_per_checkpoint == 0):
+                validate(model, criterion, valset, iteration,
+                         hparams.batch_size, n_gpus, collate_fn, logger,
+                         hparams.distributed_run, rank)
+                if rank == 0:
+                    checkpoint_path = os.path.join(
+                        output_directory, "checkpoint_{}".format(iteration))
+                    save_checkpoint(model, optimizer, learning_rate, iteration,
+                                    checkpoint_path)
+
+            iteration += 1
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-o', '--output_directory', type=str,
+                        help='directory to save checkpoints')
+    parser.add_argument('-l', '--log_directory', type=str,
+                        help='directory to save tensorboard logs')
+    parser.add_argument('-c', '--checkpoint_path', type=str, default=None,
+                        required=False, help='checkpoint path')
+    parser.add_argument('--warm_start', action='store_true',
+                        help='load model weights only, ignore specified layers')
+    parser.add_argument('--n_gpus', type=int, default=1,
+                        required=False, help='number of gpus')
+    parser.add_argument('--rank', type=int, default=0,
+                        required=False, help='rank of current gpu')
+    parser.add_argument('--group_name', type=str, default='group_name',
+                        required=False, help='Distributed group name')
+    parser.add_argument('--hparams', type=str,
+                        required=False, help='comma separated name=value pairs')
+
+    args = parser.parse_args()
+    hparams = create_hparams(args.hparams)
+
+    torch.backends.cudnn.enabled = hparams.cudnn_enabled
+    torch.backends.cudnn.benchmark = hparams.cudnn_benchmark
+
+    print("FP16 Run:", hparams.fp16_run)
+    print("Dynamic Loss Scaling:", hparams.dynamic_loss_scaling)
+    print("Distributed Run:", hparams.distributed_run)
+    print("cuDNN Enabled:", hparams.cudnn_enabled)
+    print("cuDNN Benchmark:", hparams.cudnn_benchmark)
+
+    train(args.output_directory, args.log_directory, args.checkpoint_path,
+          args.warm_start, args.n_gpus, args.rank, args.group_name, hparams)
diff --git a/utils.py b/utils.py
index c8485ba863142f933fccbe708f52fb6a2d76b0b8..43952018447c94684b3821b759aa50e001c0bd1b 100644
--- a/utils.py
+++ b/utils.py
@@ -2,12 +2,10 @@ import numpy as np
 from scipy.io.wavfile import read
 import torch
 
-use_cuda = torch.cuda.is_available()
-device = torch.device('cuda' if use_cuda else 'cpu') 
 
 def get_mask_from_lengths(lengths):
     max_len = torch.max(lengths).item()
-    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len) if use_cuda else torch.LongTensor(max_len))
+    ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len))
     mask = (ids < lengths.unsqueeze(1)).bool()
     return mask