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parakeet-ctc-0.6b-ja-coreml

Automatic Speech Recognition
Core ML
Japanese
speech
audio
parakeet
ctc
japanese
Model card Files
xet
 Community
parakeet-ctc-0.6b-ja-coreml
File size: 3,061 Bytes
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program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
{
    func main<ios17>(tensor<fp32, [1, 1024, 188]> encoder_output) {
            tensor<string, []> conv_output_pad_type_0 = const()[name = tensor<string, []>("conv_output_pad_type_0"), val = tensor<string, []>("valid")];
            tensor<int32, [1]> conv_output_strides_0 = const()[name = tensor<string, []>("conv_output_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> conv_output_pad_0 = const()[name = tensor<string, []>("conv_output_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_output_dilations_0 = const()[name = tensor<string, []>("conv_output_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, []> conv_output_groups_0 = const()[name = tensor<string, []>("conv_output_groups_0"), val = tensor<int32, []>(1)];
            tensor<string, []> encoder_output_to_fp16_dtype_0 = const()[name = tensor<string, []>("encoder_output_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
            tensor<fp16, [3073, 1024, 1]> module_decoder_layers_0_weight_to_fp16 = const()[name = tensor<string, []>("module_decoder_layers_0_weight_to_fp16"), val = tensor<fp16, [3073, 1024, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [3073]> module_decoder_layers_0_bias_to_fp16 = const()[name = tensor<string, []>("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [3073]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6293632)))];
            tensor<fp16, [1, 1024, 188]> encoder_output_to_fp16 = cast(dtype = encoder_output_to_fp16_dtype_0, x = encoder_output)[name = tensor<string, []>("cast_1")];
            tensor<fp16, [1, 3073, 188]> conv_output_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = conv_output_dilations_0, groups = conv_output_groups_0, pad = conv_output_pad_0, pad_type = conv_output_pad_type_0, strides = conv_output_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_output_to_fp16)[name = tensor<string, []>("conv_output_cast_fp16")];
            tensor<int32, [3]> var_18_perm_0 = const()[name = tensor<string, []>("op_18_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<string, []> var_18_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_18_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
            tensor<fp16, [1, 188, 3073]> var_18_cast_fp16 = transpose(perm = var_18_perm_0, x = conv_output_cast_fp16)[name = tensor<string, []>("transpose_0")];
            tensor<fp32, [1, 188, 3073]> ctc_logits = cast(dtype = var_18_cast_fp16_to_fp32_dtype_0, x = var_18_cast_fp16)[name = tensor<string, []>("cast_0")];
        } -> (ctc_logits);
}