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seamless-m4t-v2-t2tt

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  1. README.md +155 -0
  2. added_tokens.json +100 -0
  3. config.json +91 -0
  4. generation_config.json +0 -0
  5. model-00001-of-00003.safetensors +3 -0
  6. model-00002-of-00003.safetensors +3 -0
  7. model-00003-of-00003.safetensors +3 -0
  8. model.safetensors.index.json +1020 -0
  9. preprocessor_config.json +111 -0
  10. sentencepiece.bpe.model +3 -0
  11. special_tokens_map.json +144 -0
  12. spm_char_lang38_tc.model +3 -0
  13. tokenizer.model +3 -0
  14. tokenizer_config.json +933 -0
README.md ADDED
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1
+ ---
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+ license: cc-by-nc-4.0
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+ ---
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+
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+ # SeamlessM4T-v2 T2TT Lite Model
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+
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+ Extracted from `facebook/seamless-m4t-v2-large`, containing only T2TT (Text-to-Text Translation) components.
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+
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+ > Original Model: [facebook/seamless-m4t-v2-large](https://huggingface.co/facebook/seamless-m4t-v2-large)
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+ >
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+ > Official Documentation: [SeamlessM4T-v2 Documentation](https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2)
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+
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+ Note: This package only reorganizes publicly available weights from Meta's original model for T2TT usage. No new training or fine-tuning is introduced. All rights of the model and weights belong to their original owner.
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+
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+ ## Supported Features
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+
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+ - **T2TT (Text-to-Text Translation)**: Multilingual text translation
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+ - **96 Languages**: Supports text translation between 96 languages
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+
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+ ## Included Components
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+
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+ ### Model Weights
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+ - `text_encoder`: Text encoder
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+ - `text_decoder`: Text decoder
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+ - `shared.weight`: Shared word embeddings
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+ - `lang_embed`: Language embeddings
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+
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+ ## Model Size
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+
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+ - Original Model: ~8.6 GB
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+ - Lite Model: ~5.1 GB
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+ - Removed Weights: 1219 (speech_encoder, t2u_model, vocoder)
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+ - Space Saved: ~3.5 GB
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+
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+ ## Usage Examples
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+
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+ ### 1. Basic T2TT: Text-to-Text Translation
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+
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+ ```python
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+ from transformers import SeamlessM4Tv2Model, AutoProcessor
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+
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+ # Load model
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+ model = SeamlessM4Tv2Model.from_pretrained("jaman21/seamless-m4t-v2-t2tt")
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+ processor = AutoProcessor.from_pretrained("jaman21/seamless-m4t-v2-t2tt")
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+
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+ # Translate text
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+ text_inputs = processor(text="Hello, how are you?", src_lang="eng", return_tensors="pt")
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+ output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
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+ translated_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
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+ print(translated_text) # "Bonjour, comment allez-vous?"
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+ ```
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+
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+ ### 2. Advanced Generation Strategies
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+
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+ ```python
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+ # Beam search for better quality (slower)
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+ text_inputs = processor(text="The quick brown fox jumps", src_lang="eng", return_tensors="pt")
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+ outputs = model.generate(
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+ **text_inputs,
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+ tgt_lang="jpn",
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+ generate_speech=False,
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+ num_beams=5, # Use beam search
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+ max_new_tokens=256,
64
+ early_stopping=True
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+ )
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+
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+ # Sampling for more diverse output
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+ outputs = model.generate(
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+ **text_inputs,
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+ tgt_lang="kor",
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+ generate_speech=False,
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+ do_sample=True, # Enable sampling
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+ top_k=50,
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+ top_p=0.95,
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+ temperature=0.8 # 0.0-1.0: lower is more deterministic, higher is more random (affects translation quality)
76
+ )
77
+ ```
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+
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+ ### 3. Batch Processing Multiple Texts
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+
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+ ```python
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+ # Process multiple texts at once
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+ texts = [
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+ "Hello, how are you?",
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+ "What is your name?",
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+ "Nice to meet you!"
87
+ ]
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+
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+ text_inputs = processor(text=texts, src_lang="eng", return_tensors="pt", padding=True)
90
+ output_tokens = model.generate(**text_inputs, tgt_lang="ita", generate_speech=False)
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+
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+ # Decode all outputs
93
+ translations = processor.batch_decode(output_tokens, skip_special_tokens=True)
94
+ for orig, trans in zip(texts, translations):
95
+ print(f"{orig} -> {trans}")
96
+ ```
97
+
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+ ### 4. Control Generation Length and Quality
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+
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+ ```python
101
+ text_inputs = processor(text="Translate this sentence", src_lang="eng", return_tensors="pt")
102
+
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+ # Higher quality but more computationally expensive
104
+ high_quality_output = model.generate(
105
+ **text_inputs,
106
+ tgt_lang="rus",
107
+ generate_speech=False,
108
+ num_beams=5, # Beam search
109
+ max_new_tokens=512, # Allow longer output
110
+ length_penalty=1.0, # No length penalty
111
+ early_stopping=True,
112
+ use_cache=True # Accelerate generation
113
+ )
114
+
115
+ # Faster generation speed, acceptable quality
116
+ fast_output = model.generate(
117
+ **text_inputs,
118
+ tgt_lang="rus",
119
+ generate_speech=False,
120
+ num_beams=1, # Greedy decoding for better translation quality (slower)
121
+ max_new_tokens=256,
122
+ use_cache=True
123
+ )
124
+ ```
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+
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+ ### 5. GPU/CPU Usage
127
+
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+ ```python
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+ import torch
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+
131
+ # Move model to GPU if available
132
+ device = "cuda" if torch.cuda.is_available() else "cpu"
133
+ model = model.to(device)
134
+
135
+ # Process inputs on the same device
136
+ text_inputs = processor(text="Hello", src_lang="eng", return_tensors="pt")
137
+ text_inputs = {k: v.to(device) for k, v in text_inputs.items()}
138
+
139
+ # Generate
140
+ with torch.inference_mode(): # More efficient than torch.no_grad()
141
+ outputs = model.generate(**text_inputs, tgt_lang="cmn", generate_speech=False)
142
+ ```
143
+
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+ ## License
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+
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+ Same as the original model: **CC-BY-NC-4.0**
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+
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+ For commercial use, please refer to Meta's licensing terms.
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+
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+ ## References
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+
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+ - [SeamlessM4T-v2 Paper](https://arxiv.org/abs/2312.05187)
153
+ - [Official Model Card](https://huggingface.co/facebook/seamless-m4t-v2-large)
154
+ - [Transformers Documentation](https://huggingface.co/docs/transformers/en/model_doc/seamless_m4t_v2)
155
+ - [GitHub Repository](https://github.com/facebookresearch/seamless_communication)
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