rmayormartins
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Subindo arquivos
Browse files- README.md +58 -6
- app.py +50 -0
- requirements.txt +5 -0
- results/config.json +110 -0
- results/model.safetensors +3 -0
- results/preprocessor_config.json +10 -0
- results/special_tokens_map.json +6 -0
- results/tokenizer_config.json +50 -0
- results/vocab.json +34 -0
README.md
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---
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title: Speech
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emoji:
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colorFrom: blue
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colorTo:
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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license: ecl-2.0
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---
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-
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---
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title: Speech-accent-pt-br-classifier
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emoji: ποΈπ€π§π·
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: "4.12.0"
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app_file: app.py
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pinned: false
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---
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# Speech Portuguese (Brazilian) Accent Classifier
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This project is a speech accent classifier that distinguishes between Portuguese (Brazilian) and other accents.
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## Project Overview
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This application uses a trained model to classify speech accents into two categories:
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1. Portuguese (Brazilian)
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2. Other
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The model is based on the author's work [(results) brazil pt accent] and utilizes the Portuguese portion of the Common Voice dataset (version 11.0) from Mozilla Foundation.
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## Dataset
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The project uses the Portuguese subset of the Common Voice dataset:
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- Dataset: "mozilla-foundation/common_voice_11_0", "pt"
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Brazilian accents included in the dataset:
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- PortuguΓͺs do Brasil, RegiΓ£o Sul do Brasil
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- Paulistano
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- Paulista, Brasileiro
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- Carioca
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- Mato Grosso
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- Mineiro
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- Interior Paulista
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- GaΓΊcho
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- Nordestino
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- And various regional mixes
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## Technical Details
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The project utilizes the following model and processor:
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- Model: "facebook/wav2vec2-base-960h"
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- Processor: Wav2Vec2Processor.from_pretrained
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## License
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ecl
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## Developer Information
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Developed by Ramon Mayor Martins (2024)
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- Email: rmayormartins@gmail.com
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- Homepage: https://rmayormartins.github.io/
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- Twitter: @rmayormartins
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- GitHub: https://github.com/rmayormartins
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## Acknowledgements
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Special thanks to Instituto Federal de Santa Catarina (Federal Institute of Santa Catarina) IFSC-SΓ£o JosΓ©-Brazil.
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## Contact
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For any queries or suggestions, please contact the developer using the information provided above.
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app.py
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import gradio as gr
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import torch
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import numpy as np
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from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
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# modelo e o processador salvos
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model_name = "results"
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processor = Wav2Vec2Processor.from_pretrained(model_name)
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model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
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def classify_accent(audio):
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if audio is None:
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return "Erro: Nenhum Γ‘udio recebido"
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# entrada
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print(f"Tipo de entrada de Γ‘udio: {type(audio)}")
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# O Γ‘udio formato
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print(f"Received audio input: {audio}")
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try:
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audio_array = audio[1] # O Γ‘udio da tupla
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sample_rate = audio[0] # A taxa de amostragem da tupla
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print(f"Shape do Γ‘udio: {audio_array.shape}, Taxa de amostragem: {sample_rate}")
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#
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audio_array = audio_array.astype(np.float32)
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# taxa de amostragem
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if sample_rate != 16000:
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import librosa
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audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=16000)
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input_values = processor(audio_array, return_tensors="pt", sampling_rate=16000).input_values
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# Inf
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with torch.no_grad():
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logits = model(input_values).logits
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predicted_ids = torch.argmax(logits, dim=-1).item()
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# ids accent
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labels = ["Brazilian", "Outro"]
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return labels[predicted_ids]
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except Exception as e:
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return f"Erro ao processar o Γ‘udio: {str(e)}"
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# Interface do Gradio
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interface = gr.Interface(fn=classify_accent, inputs=gr.Audio(type="numpy"), outputs="label")
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interface.launch()
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requirements.txt
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gradio==4.29.0
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torch
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transformers
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librosa
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numpy
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results/config.json
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{
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"_name_or_path": "facebook/wav2vec2-base-960h",
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"activation_dropout": 0.1,
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"adapter_attn_dim": null,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 256,
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"contrastive_logits_temperature": 0.1,
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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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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "sum",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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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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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "group",
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"feat_proj_dropout": 0.1,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.1,
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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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"initializer_range": 0.02,
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"intermediate_size": 3072,
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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_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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"model_type": "wav2vec2",
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"num_adapter_layers": 3,
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"num_attention_heads": 12,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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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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"num_negatives": 100,
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"output_hidden_size": 768,
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"pad_token_id": 0,
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"proj_codevector_dim": 256,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_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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1500
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],
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"tdnn_kernel": [
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5,
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3,
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3,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.41.2",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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}
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results/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b2832c855da20b63ee6353ba032d27bd9c5c4920c64bc1fe49fe5d498f6f87d0
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size 378302360
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results/preprocessor_config.json
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "Wav2Vec2Processor",
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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results/special_tokens_map.json
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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results/tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<pad>",
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"lstrip": true,
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"normalized": false,
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"rstrip": true,
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+
"single_word": false,
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"special": false
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},
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"1": {
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"content": "<s>",
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+
"lstrip": true,
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+
"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"2": {
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"content": "</s>",
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+
"lstrip": true,
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+
"normalized": false,
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"rstrip": true,
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"single_word": false,
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"special": false
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},
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"3": {
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"content": "<unk>",
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+
"lstrip": true,
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+
"normalized": false,
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"rstrip": true,
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+
"single_word": false,
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"special": false
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}
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+
},
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36 |
+
"bos_token": "<s>",
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37 |
+
"clean_up_tokenization_spaces": true,
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38 |
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"do_lower_case": false,
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"do_normalize": true,
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+
"eos_token": "</s>",
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41 |
+
"model_max_length": 1000000000000000019884624838656,
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+
"pad_token": "<pad>",
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+
"processor_class": "Wav2Vec2Processor",
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+
"replace_word_delimiter_char": " ",
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"return_attention_mask": false,
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"target_lang": null,
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"tokenizer_class": "Wav2Vec2CTCTokenizer",
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"unk_token": "<unk>",
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"word_delimiter_token": "|"
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}
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results/vocab.json
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1 |
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{
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2 |
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3 |
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4 |
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5 |
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8 |
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10 |
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11 |
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12 |
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15 |
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17 |
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18 |
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19 |
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20 |
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22 |
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23 |
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24 |
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25 |
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26 |
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28 |
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29 |
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30 |
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31 |
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32 |
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33 |
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"|": 4
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34 |
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}
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