Hugging Face's logo

m3hrdadfi
/
wav2vec2-base-100k-eating-sound-collection

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
audio
audio-classification
Inference Endpoints
Model card Files Community
m3hrdadfi commited on
Commit
810ce8b
•
1 Parent(s): 0c6c5aa

Initial model

Browse files
Files changed (10) hide show
  1. README.md +134 -0
  2. all_results.json +13 -0
  3. config.json +116 -0
  4. eval_results.json +9 -0
  5. predict_results.txt +2229 -0
  6. preprocessor_config.json +9 -0
  7. pytorch_model.bin +3 -0
  8. train_results.json +7 -0
  9. trainer_state.json +471 -0
  10. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ tags:
3
+ - audio
4
+ - automatic-speech-recognition
5
+ - speech
6
+ - speech-classification
7
+ license: apache-2.0
8
+ ---
9
+
10
+ # Eating Sound Collection using Wav2Vec 2.0
11
+
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+
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+ ## How to use
14
+
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+ ### Requirements
16
+
17
+ ```bash
18
+ # requirement packages
19
+ !pip install git+https://github.com/huggingface/datasets.git
20
+ !pip install git+https://github.com/huggingface/transformers.git
21
+ !pip install torchaudio
22
+ !pip install librosa
23
+ ```
24
+
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+ ### Prediction
26
+
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+ ```python
28
+ import torch
29
+ import torch.nn as nn
30
+ import torch.nn.functional as F
31
+ import torchaudio
32
+ from transformers import AutoConfig, Wav2Vec2FeatureExtractor
33
+
34
+ import librosa
35
+ import IPython.display as ipd
36
+ import numpy as np
37
+ import pandas as pd
38
+ ```
39
+
40
+ ```python
41
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
42
+ model_name_or_path = "m3hrdadfi/wav2vec2-base-100k-eating-sound-collection"
43
+ config = AutoConfig.from_pretrained(model_name_or_path)
44
+ feature_extractor = Wav2Vec2Processor.from_pretrained(model_name_or_path)
45
+ sampling_rate = feature_extractor.sampling_rate
46
+ model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
47
+ ```
48
+
49
+ ```python
50
+ def speech_file_to_array_fn(path, sampling_rate):
51
+ speech_array, _sampling_rate = torchaudio.load(path)
52
+ resampler = torchaudio.transforms.Resample(_sampling_rate)
53
+ speech = resampler(speech_array).squeeze().numpy()
54
+ return speech
55
+
56
+
57
+ def predict(path, sampling_rate):
58
+ speech = speech_file_to_array_fn(path, sampling_rate)
59
+ features = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
60
+
61
+ input_values = features.input_values.to(device)
62
+
63
+ with torch.no_grad():
64
+ logits = model(input_values).logits
65
+
66
+ scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
67
+ outputs = [{"Label": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
68
+ return outputs
69
+ ```
70
+
71
+ ```python
72
+ path = "clips_rd/gummies/gummies_6_04.wav"
73
+ outputs = predict(path, sampling_rate)
74
+ ```
75
+
76
+ ```bash
77
+ [
78
+ {'Label': 'aloe', 'Score': '0.0%'},
79
+ {'Label': 'burger', 'Score': '0.0%'},
80
+ {'Label': 'cabbage', 'Score': '0.0%'},
81
+ {'Label': 'candied_fruits', 'Score': '0.0%'},
82
+ {'Label': 'carrots', 'Score': '0.0%'},
83
+ {'Label': 'chips', 'Score': '0.0%'},
84
+ {'Label': 'chocolate', 'Score': '0.0%'},
85
+ {'Label': 'drinks', 'Score': '0.0%'},
86
+ {'Label': 'fries', 'Score': '0.0%'},
87
+ {'Label': 'grapes', 'Score': '0.0%'},
88
+ {'Label': 'gummies', 'Score': '99.8%'},
89
+ {'Label': 'ice-cream', 'Score': '0.0%'},
90
+ {'Label': 'jelly', 'Score': '0.1%'},
91
+ {'Label': 'noodles', 'Score': '0.0%'},
92
+ {'Label': 'pickles', 'Score': '0.0%'},
93
+ {'Label': 'pizza', 'Score': '0.0%'},
94
+ {'Label': 'ribs', 'Score': '0.0%'},
95
+ {'Label': 'salmon', 'Score': '0.0%'},
96
+ {'Label': 'soup', 'Score': '0.0%'},
97
+ {'Label': 'wings', 'Score': '0.0%'}
98
+ ]
99
+ ```
100
+
101
+
102
+ ## Evaluation
103
+ The following tables summarize the scores obtained by model overall and per each class.
104
+
105
+
106
+ | label | precision | recall | f1-score | support |
107
+ |:--------------:|:---------:|:------:|:--------:|:-------:|
108
+ | aloe | 0.989 | 0.807 | 0.889 | 109 |
109
+ | burger | 1.000 | 0.471 | 0.640 | 119 |
110
+ | cabbage | 0.907 | 0.970 | 0.937 | 100 |
111
+ | candied_fruits | 0.952 | 0.988 | 0.970 | 161 |
112
+ | carrots | 0.970 | 0.992 | 0.981 | 132 |
113
+ | chips | 0.993 | 0.951 | 0.972 | 144 |
114
+ | chocolate | 0.828 | 0.914 | 0.869 | 58 |
115
+ | drinks | 0.982 | 0.948 | 0.965 | 58 |
116
+ | fries | 0.935 | 0.783 | 0.852 | 129 |
117
+ | grapes | 0.965 | 0.940 | 0.952 | 116 |
118
+ | gummies | 0.880 | 0.971 | 0.923 | 136 |
119
+ | ice-cream | 0.953 | 0.972 | 0.962 | 145 |
120
+ | jelly | 0.906 | 0.875 | 0.890 | 88 |
121
+ | noodles | 0.817 | 0.817 | 0.817 | 82 |
122
+ | pickles | 0.933 | 0.960 | 0.946 | 174 |
123
+ | pizza | 0.704 | 0.934 | 0.803 | 122 |
124
+ | ribs | 0.796 | 0.755 | 0.775 | 98 |
125
+ | salmon | 0.647 | 0.970 | 0.776 | 100 |
126
+ | soup | 0.941 | 0.857 | 0.897 | 56 |
127
+ | wings | 0.842 | 0.792 | 0.816 | 101 |
128
+ | accuracy | 0.890 | 0.890 | 0.890 | 0 |
129
+ | macro avg | 0.897 | 0.883 | 0.882 | 2228 |
130
+ | weighted avg | 0.903 | 0.890 | 0.888 | 2228 |
131
+
132
+
133
+ ## Questions?
134
+ Post a Github issue from [HERE](https://github.com/m3hrdadfi/soxan/issues).
all_results.json ADDED
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+ {
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+ "eval_accuracy": 0.8936265707015991,
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+ "eval_loss": 0.7216407060623169,
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+ "eval_runtime": 257.0433,
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+ "eval_samples": 2228,
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+ "eval_samples_per_second": 8.668,
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+ "eval_steps_per_second": 4.334,
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+ "train_runtime": 390.3998,
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+ "train_samples": 8912,
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+ "train_samples_per_second": 159.795,
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+ "train_steps_per_second": 39.949
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+ }
config.json ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "/notebooks/checkpoint-15500",
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+ "activation_dropout": 0.1,
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+ "apply_spec_augment": true,
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+ "architectures": [
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+ "Wav2Vec2ForSpeechClassification"
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+ ],
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+ "attention_dropout": 0.1,
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+ "bos_token_id": 1,
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+ "conv_bias": false,
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+ "conv_dim": [
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512
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+ "conv_kernel": [
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+ ],
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+ "ctc_loss_reduction": "sum",
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+ "ctc_zero_infinity": false,
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+ "do_stable_layer_norm": false,
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+ "eos_token_id": 2,
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+ "feat_extract_activation": "gelu",
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.1,
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
54
+ "0": "aloe",
55
+ "1": "burger",
56
+ "2": "cabbage",
57
+ "3": "candied_fruits",
58
+ "4": "carrots",
59
+ "5": "chips",
60
+ "6": "chocolate",
61
+ "7": "drinks",
62
+ "8": "fries",
63
+ "9": "grapes",
64
+ "10": "gummies",
65
+ "11": "ice-cream",
66
+ "12": "jelly",
67
+ "13": "noodles",
68
+ "14": "pickles",
69
+ "15": "pizza",
70
+ "16": "ribs",
71
+ "17": "salmon",
72
+ "18": "soup",
73
+ "19": "wings"
74
+ },
75
+ "initializer_range": 0.02,
76
+ "intermediate_size": 3072,
77
+ "label2id": {
78
+ "aloe": 0,
79
+ "burger": 1,
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+ "cabbage": 2,
81
+ "candied_fruits": 3,
82
+ "carrots": 4,
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+ "chips": 5,
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+ "chocolate": 6,
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+ "drinks": 7,
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+ "fries": 8,
87
+ "grapes": 9,
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+ "gummies": 10,
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+ "ice-cream": 11,
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+ "jelly": 12,
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+ "noodles": 13,
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+ "pickles": 14,
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+ "pizza": 15,
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+ "ribs": 16,
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+ "salmon": 17,
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+ "soup": 18,
97
+ "wings": 19
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.1,
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+ "mask_feature_length": 10,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_prob": 0.05,
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 12,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "pooling_mode": "mean",
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+ "problem_type": "single_label_classification",
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+ "transformers_version": "4.7.0.dev0",
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+ "vocab_size": 32
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+ }
eval_results.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "epoch": 7.0,
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+ "eval_accuracy": 0.8936265707015991,
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+ "eval_loss": 0.7216407060623169,
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+ "eval_runtime": 257.0433,
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+ "eval_samples": 2228,
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+ "eval_samples_per_second": 8.668,
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+ "eval_steps_per_second": 4.334
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+ }
predict_results.txt ADDED
@@ -0,0 +1,2229 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ index prediction
2
+ 0 cabbage
3
+ 1 pizza
4
+ 2 aloe
5
+ 3 candied_fruits
6
+ 4 chocolate
7
+ 5 pickles
8
+ 6 fries
9
+ 7 fries
10
+ 8 chips
11
+ 9 burger
12
+ 10 ice-cream
13
+ 11 carrots
14
+ 12 gummies
15
+ 13 candied_fruits
16
+ 14 grapes
17
+ 15 chips
18
+ 16 gummies
19
+ 17 ribs
20
+ 18 grapes
21
+ 19 gummies
22
+ 20 ice-cream
23
+ 21 ribs
24
+ 22 pizza
25
+ 23 grapes
26
+ 24 aloe
27
+ 25 chips
28
+ 26 aloe
29
+ 27 pizza
30
+ 28 pickles
31
+ 29 pizza
32
+ 30 aloe
33
+ 31 gummies
34
+ 32 ice-cream
35
+ 33 burger
36
+ 34 pickles
37
+ 35 carrots
38
+ 36 cabbage
39
+ 37 pizza
40
+ 38 gummies
41
+ 39 noodles
42
+ 40 aloe
43
+ 41 carrots
44
+ 42 candied_fruits
45
+ 43 aloe
46
+ 44 drinks
47
+ 45 salmon
48
+ 46 pizza
49
+ 47 jelly
50
+ 48 drinks
51
+ 49 drinks
52
+ 50 candied_fruits
53
+ 51 fries
54
+ 52 noodles
55
+ 53 salmon
56
+ 54 chips
57
+ 55 ribs
58
+ 56 ribs
59
+ 57 pickles
60
+ 58 salmon
61
+ 59 noodles
62
+ 60 grapes
63
+ 61 pizza
64
+ 62 chips
65
+ 63 jelly
66
+ 64 ice-cream
67
+ 65 salmon
68
+ 66 salmon
69
+ 67 pickles
70
+ 68 candied_fruits
71
+ 69 pizza
72
+ 70 pickles
73
+ 71 pickles
74
+ 72 jelly
75
+ 73 gummies
76
+ 74 salmon
77
+ 75 fries
78
+ 76 noodles
79
+ 77 cabbage
80
+ 78 salmon
81
+ 79 carrots
82
+ 80 chips
83
+ 81 chocolate
84
+ 82 carrots
85
+ 83 fries
86
+ 84 soup
87
+ 85 burger
88
+ 86 cabbage
89
+ 87 ice-cream
90
+ 88 soup
91
+ 89 carrots
92
+ 90 wings
93
+ 91 jelly
94
+ 92 carrots
95
+ 93 wings
96
+ 94 wings
97
+ 95 jelly
98
+ 96 pickles
99
+ 97 cabbage
100
+ 98 candied_fruits
101
+ 99 pizza
102
+ 100 cabbage
103
+ 101 soup
104
+ 102 fries
105
+ 103 soup
106
+ 104 noodles
107
+ 105 gummies
108
+ 106 chocolate
109
+ 107 candied_fruits
110
+ 108 chocolate
111
+ 109 chocolate
112
+ 110 pickles
113
+ 111 grapes
114
+ 112 pickles
115
+ 113 candied_fruits
116
+ 114 grapes
117
+ 115 pizza
118
+ 116 pizza
119
+ 117 carrots
120
+ 118 drinks
121
+ 119 chips
122
+ 120 wings
123
+ 121 aloe
124
+ 122 cabbage
125
+ 123 grapes
126
+ 124 candied_fruits
127
+ 125 ice-cream
128
+ 126 pickles
129
+ 127 gummies
130
+ 128 fries
131
+ 129 wings
132
+ 130 pizza
133
+ 131 pizza
134
+ 132 ice-cream
135
+ 133 gummies
136
+ 134 wings
137
+ 135 carrots
138
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