Text-to-Speech
Transformers
Safetensors
rumik_oss
text-generation
tts
indic
expressive
mimi
speech
custom_code
Instructions to use arvindcodex11/MYRA-oss-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arvindcodex11/MYRA-oss-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="arvindcodex11/MYRA-oss-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("arvindcodex11/MYRA-oss-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,184 Bytes
919e62d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"_frame_rate": 12.5,
"architectures": [
"MimiModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"audio_channels": 1,
"codebook_dim": 256,
"codebook_size": 2048,
"compress": 2,
"dilation_growth_rate": 2,
"dtype": "float32",
"head_dim": 64,
"hidden_act": "gelu",
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 2048,
"kernel_size": 7,
"last_kernel_size": 3,
"layer_scale_initial_scale": 0.01,
"max_position_embeddings": 8000,
"model_type": "mimi",
"norm_eps": 1e-05,
"normalize": false,
"num_attention_heads": 8,
"num_filters": 64,
"num_hidden_layers": 8,
"num_key_value_heads": 8,
"num_quantizers": 32,
"num_residual_layers": 1,
"num_semantic_quantizers": 1,
"pad_mode": "constant",
"residual_kernel_size": 3,
"rope_theta": 10000.0,
"sampling_rate": 24000,
"sliding_window": 250,
"transformers_version": "4.57.6",
"trim_right_ratio": 1.0,
"upsample_groups": 512,
"upsampling_ratios": [
8,
6,
5,
4
],
"use_cache": false,
"use_causal_conv": true,
"use_conv_shortcut": false,
"use_streaming": false,
"vector_quantization_hidden_dimension": 256
}
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