{
  "sample_rate": 24000,
  "frame_samples": 1920,
  "frame_rate": 12.5,
  "max_context": 512,
  "prefill_max": 128,
  "programs": {
    "flow_lm": {
      "forward": {
        "inputs": [
          {
            "shape": [
              1,
              128,
              512
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1
            ],
            "dtype": "long"
          },
          {
            "shape": [
              1,
              32
            ],
            "dtype": "float"
          }
        ],
        "outputs": [
          {
            "shape": [
              1,
              32
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              1
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              1,
              512
            ],
            "dtype": "float"
          }
        ]
      }
    },
    "mimi": {
      "forward": {
        "inputs": [
          {
            "shape": [
              1,
              1,
              32
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              512,
              16
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              1,
              1,
              250
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              512,
              6
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              256,
              6
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              256,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              128,
              5
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              128,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              4
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              2
            ],
            "dtype": "float"
          }
        ],
        "outputs": [
          {
            "shape": [
              1,
              1,
              1920
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              512,
              16
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              8,
              250,
              64
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              1,
              1,
              250
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              512,
              6
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              256,
              6
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              256,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              128,
              5
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              128,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              4
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              2
            ],
            "dtype": "float"
          },
          {
            "shape": [
              1,
              64,
              2
            ],
            "dtype": "float"
          }
        ]
      }
    }
  },
  "flow_lm": "forward(embeds, input_pos, noise): writes KV positions [input_pos, input_pos + S); outputs are for the last position. Calls: voice prompt (S = 125) at 0, text prompt (S = #tokens) at 125, then one call per frame with S = 1: the first takes phonon_bos_embed, each later one the previous call's next_embeds",
  "mimi": "forward(latent, *state) -> (pcm, *state): pass the previous call's states back; zeros to start",
  "mimi_state": [
    [
      "upsample_partial",
      [
        1,
        512,
        16
      ]
    ],
    [
      "k0",
      [
        1,
        8,
        250,
        64
      ]
    ],
    [
      "v0",
      [
        1,
        8,
        250,
        64
      ]
    ],
    [
      "k1",
      [
        1,
        8,
        250,
        64
      ]
    ],
    [
      "v1",
      [
        1,
        8,
        250,
        64
      ]
    ],
    [
      "kv_seen",
      [
        1,
        1,
        1,
        250
      ]
    ],
    [
      "rope_offset",
      [
        1
      ]
    ],
    [
      "init_conv_prev",
      [
        1,
        512,
        6
      ]
    ],
    [
      "up1_partial",
      [
        1,
        256,
        6
      ]
    ],
    [
      "res1_prev",
      [
        1,
        256,
        2
      ]
    ],
    [
      "up2_partial",
      [
        1,
        128,
        5
      ]
    ],
    [
      "res2_prev",
      [
        1,
        128,
        2
      ]
    ],
    [
      "up3_partial",
      [
        1,
        64,
        4
      ]
    ],
    [
      "res3_prev",
      [
        1,
        64,
        2
      ]
    ],
    [
      "final_conv_prev",
      [
        1,
        64,
        2
      ]
    ]
  ],
  "text": "normalize (the official runtime's English rules; identity for plain sentences), then trim, collapse whitespace, capitalize the first letter, append '.' if the text ends with a letter or digit; encode with tokenizer.json without special tokens; embeds[i] = bf16 table row tokens[i] (as f32)",
  "noise": "32 values per frame, N(0, temperature); 0.3 is the official runtime's default, 0 makes it deterministic",
  "eos": "stop after frames_after_eos more frames once eos_logit > 4.0 (frames_after_eos = 3 for texts of <= 4 words, else 1); the EOS frame and its tail are decoded",
  "frame_budget": "ceil((#tokens / 3 + 2) * 12.5) frames at most",
  "voices": {
    "default": {
      "file": "voice_default.f32.bin",
      "shape": [
        125,
        512
      ]
    },
    "en_us_easton": {
      "file": "voice_en_us_easton.f32.bin",
      "shape": [
        125,
        512
      ]
    }
  },
  "conditions": {
    "num_speakers": "1",
    "duration_delta": 0.0,
    "note": "the runtime's defaults, folded into the programs"
  }
}