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--- |
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base_model: unsloth/csm-1b |
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tags: |
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- transformers |
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- csm |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- beyoru/kafka-voice |
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--- |
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# Usage |
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``` |
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import torch |
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from transformers import CsmForConditionalGeneration, AutoProcessor |
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model_id = "beyoru/kafka-sesame" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# load the model and the processor |
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processor = AutoProcessor.from_pretrained(model_id) |
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model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device) |
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model.eval() |
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model.generation_config.max_length = 250 # big enough to avoid recompilation |
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model.generation_config.max_new_tokens = None # would take precedence over max_length |
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model.generation_config.cache_implementation = "static" |
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model.depth_decoder.generation_config.cache_implementation = "static" |
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# prepare the inputs |
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text = "[0]Hello from Sesame." # `[0]` for speaker id 0 |
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inputs = processor(text, add_special_tokens=True).to(device) |
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# another equivalent way to prepare the inputs |
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conversation = [ |
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{"role": "0", "content": [{"type": "text", "text": "Hello from Sesame."}]}, |
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] |
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inputs = processor.apply_chat_template( |
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conversation, |
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tokenize=True, |
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return_dict=True, |
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).to(device) |
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# infer the model |
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@torch.interface_mode() |
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audio = model.generate(**inputs, output_audio=True) |
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processor.save_audio(audio, "example_without_context.wav") |
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``` |
