Upload folder using huggingface_hub

push_to_hub.py

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+from transformers import Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
+import json
+
+# Path to your local model directory and vocab file
+local_model_path = './wav2vec2-large-mal'  # Directory with model checkpoints
+vocab_path = './vocab.json'  # Path to your vocab.json file
+
+# Hugging Face model ID (replace with your username)
+model_id = "aoxo/wav2vec2-large-mal"
+
+# Load vocab
+with open(vocab_path, 'r') as f:
+    vocab_dict = json.load(f)
+
+# Create custom tokenizer
+tokenizer = Wav2Vec2CTCTokenizer(
+    vocab_path, 
+    unk_token="[UNK]", 
+    pad_token="[PAD]", 
+    word_delimiter_token="|"
+)
+
+# Create feature extractor
+feature_extractor = Wav2Vec2FeatureExtractor(
+    feature_size=1, 
+    sampling_rate=16000, 
+    padding_value=0.0, 
+    do_normalize=True, 
+    return_attention_mask=False
+)
+
+# Create processor
+processor = Wav2Vec2Processor(
+    feature_extractor=feature_extractor, 
+    tokenizer=tokenizer
+)
+
+# Load the model from the checkpoint directory
+model = Wav2Vec2ForCTC.from_pretrained(local_model_path)
+
+# Push to Hugging Face Hub
+model.push_to_hub(model_id)
+processor.push_to_hub(model_id)
+tokenizer.push_to_hub(model_id)
+
+print(f"Model, processor, and tokenizer successfully pushed to {model_id}")