[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
         always_run: true
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
         args: ['--maxkb=512']
@@ -31,22 +31,22 @@ repos:
         exclude: '^(ggml/|ggml-patches/|proto/riva-common/).*'
 
   - repo: https://github.com/pre-commit/mirrors-clang-format
-    rev: v18.1.8
+    rev: v22.1.8
     hooks:
       - id: clang-format
         types_or: [c++, c, cuda]
         # ggml + vendored riva protos are upstream code — don't reformat.
         exclude: '^(ggml/|ggml-patches/|proto/riva-common/|build/|build-.*/).*'
 
-  - repo: https://github.com/psf/black
-    rev: 24.10.0
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 26.5.1
     hooks:
       - id: black
         args: ['--skip-string-normalization', '--line-length=100']
         exclude: '^(ggml/|proto/riva-common/|build/).*'
 
   - repo: https://github.com/pycqa/isort
-    rev: 5.13.2
+    rev: 9.0.0b5
     hooks:
       - id: isort
         name: isort (python)
@@ -58,7 +58,7 @@ repos:
           - '--skip=build'
 
   - repo: https://github.com/shellcheck-py/shellcheck-py
-    rev: v0.10.0.1
+    rev: v0.11.0.1-1
     hooks:
       - id: shellcheck
         args: ['--severity=warning']

app/health.cpp

@@ -111,10 +111,11 @@ command_health(int argc, char** argv) {
             const auto retry_time = std::chrono::steady_clock::now();
             if (retry_time >= deadline)
                 break;
-            std::this_thread::sleep_for(std::min(
-                std::chrono::duration_cast<std::chrono::steady_clock::duration>(
-                    std::chrono::milliseconds(250)),
-                deadline - retry_time));
+            std::this_thread::sleep_for(
+                std::min(
+                    std::chrono::duration_cast<std::chrono::steady_clock::duration>(
+                        std::chrono::milliseconds(250)),
+                    deadline - retry_time));
         } while (true);
         return print_cli_error(
             "health", "not ready (" + last_error + ")", kCliExitRuntime, "not_ready");

conversion/asr.py

@@ -26,6 +26,7 @@
 If `--head-type` is omitted, the head is auto-detected from the model_config.yaml
 inside the .nemo (presence of `joint` block ⇒ RNNT, `labels` only ⇒ CTC).
 """
+
 import math
 import re
 import sys

conversion/pnc.py

@@ -8,6 +8,7 @@
 Emits arch="pnc": a BERT encoder + two token-classification heads (punct, capit)
 under the `pnc.*` namespace, plus the WordPiece vocab and label sets.
 """
+
 import tempfile
 from pathlib import Path
 from typing import Optional

conversion/vad.py

@@ -45,6 +45,7 @@
 Both sources funnel through one emit routine keyed by the original silero
 state_dict tensor names.
 """
+
 import struct
 import sys
 from pathlib import Path

src/asr/decoders/rnnt_modules.cpp

@@ -245,8 +245,9 @@ RnntJointModule::build_joint_tail(
     auto logits = out_proj_->build_graph(session, act_bag, tc);
     if (B > 1) {
         auto l = logits.get_tensor(0);
-        logits.set_first_tensor(ggml_runtime::ggml_bf_tensor(
-            ggml_reshape_3d(bf_ctx.ctx, l.tensor, l.tensor->ne[0], T, B), l.buft));
+        logits.set_first_tensor(
+            ggml_runtime::ggml_bf_tensor(
+                ggml_reshape_3d(bf_ctx.ctx, l.tensor, l.tensor->ne[0], T, B), l.buft));
     }
     if (argmax_only) {
         auto l = logits.get_tensor(0);

src/asr/model.cpp

@@ -299,10 +299,12 @@ class RnntModel::RnntDecoderStages : public ggml_runtime::Module {
                 auto c = s->model_tensor_container->get_tensor_by_name(
                     rnnt_c_state_name(active_bank, i));
                 auto bf = tc->get_ctx_of_buffer_type(h.buft);
-                pred_in.add_tensor(ggml_runtime::ggml_bf_tensor(
-                    ggml_get_rows(bf.ctx, h.tensor, slot_ids.tensor), h.buft));
-                pred_in.add_tensor(ggml_runtime::ggml_bf_tensor(
-                    ggml_get_rows(bf.ctx, c.tensor, slot_ids.tensor), c.buft));
+                pred_in.add_tensor(
+                    ggml_runtime::ggml_bf_tensor(
+                        ggml_get_rows(bf.ctx, h.tensor, slot_ids.tensor), h.buft));
+                pred_in.add_tensor(
+                    ggml_runtime::ggml_bf_tensor(
+                        ggml_get_rows(bf.ctx, c.tensor, slot_ids.tensor), c.buft));
             }
             auto pred_out = pred_->build_graph(s, pred_in, tc);
             auto pred_proj =
@@ -312,27 +314,31 @@ class RnntModel::RnntDecoderStages : public ggml_runtime::Module {
             ggml_runtime::TensorBag state_out;
             auto pred_state =
                 s->model_tensor_container->get_tensor_by_name(kRnntPredProjectionState);
-            state_out.add_tensor(ggml_runtime::ggml_bf_tensor(
-                ggml_set_rows(
-                    bf.ctx, pred_state.tensor, ggml_cont(bf.ctx, pred_proj.tensor),
-                    slot_ids.tensor),
-                pred_proj.buft));
+            state_out.add_tensor(
+                ggml_runtime::ggml_bf_tensor(
+                    ggml_set_rows(
+                        bf.ctx, pred_state.tensor, ggml_cont(bf.ctx, pred_proj.tensor),
+                        slot_ids.tensor),
+                    pred_proj.buft));
             for (int i = 0; i < cfg.pred_num_layers; i++) {
                 auto h_dst = s->model_tensor_container->get_tensor_by_name(
                     rnnt_h_state_name(candidate_bank, i));
                 auto c_dst = s->model_tensor_container->get_tensor_by_name(
                     rnnt_c_state_name(candidate_bank, i));
-                state_out.add_tensor(ggml_runtime::ggml_bf_tensor(
-                    ggml_set_rows(
-                        bf.ctx, h_dst.tensor,
-                        ggml_cont(bf.ctx, pred_out.get_tensor(1 + 2 * i).tensor), slot_ids.tensor),
-                    pred_out.get_tensor(1 + 2 * i).buft));
-                state_out.add_tensor(ggml_runtime::ggml_bf_tensor(
-                    ggml_set_rows(
-                        bf.ctx, c_dst.tensor,
-                        ggml_cont(bf.ctx, pred_out.get_tensor(1 + 2 * i + 1).tensor),
-                        slot_ids.tensor),
-                    pred_out.get_tensor(1 + 2 * i + 1).buft));
+                state_out.add_tensor(
+                    ggml_runtime::ggml_bf_tensor(
+                        ggml_set_rows(
+                            bf.ctx, h_dst.tensor,
+                            ggml_cont(bf.ctx, pred_out.get_tensor(1 + 2 * i).tensor),
+                            slot_ids.tensor),
+                        pred_out.get_tensor(1 + 2 * i).buft));
+                state_out.add_tensor(
+                    ggml_runtime::ggml_bf_tensor(
+                        ggml_set_rows(
+                            bf.ctx, c_dst.tensor,
+                            ggml_cont(bf.ctx, pred_out.get_tensor(1 + 2 * i + 1).tensor),
+                            slot_ids.tensor),
+                        pred_out.get_tensor(1 + 2 * i + 1).buft));
             }
             if (!fused_tdt && !fused_rnnt)
                 return state_out;

src/asr/vad/silero_vad.cpp

@@ -178,10 +178,12 @@ class SileroVadModule : public ggml_runtime::Module {
         ggml_runtime::TensorBag out;
         out.add_tensor(ggml_runtime::ggml_bf_tensor(probabilities, buft));
         // Commit the active recurrent-state rows in-graph.
-        out.add_tensor(ggml_runtime::ggml_bf_tensor(
-            ggml_set_rows(g, h_arena, ggml_cont(g, h_state), slot_ids.tensor), buft));
-        out.add_tensor(ggml_runtime::ggml_bf_tensor(
-            ggml_set_rows(g, c_arena, ggml_cont(g, c_state), slot_ids.tensor), buft));
+        out.add_tensor(
+            ggml_runtime::ggml_bf_tensor(
+                ggml_set_rows(g, h_arena, ggml_cont(g, h_state), slot_ids.tensor), buft));
+        out.add_tensor(
+            ggml_runtime::ggml_bf_tensor(
+                ggml_set_rows(g, c_arena, ggml_cont(g, c_state), slot_ids.tensor), buft));
         return out;
     }
 

tests/cpp/asr/test_endpointer.cpp

@@ -57,9 +57,9 @@ static void
 test_policy() {
     // Threshold 800 ms (riva default). The runner feeds (now_ms, last_speech_ms).
     {
-        VadEndpointer ep(VadEndpointerCfg{
-            /*enable=*/true, /*vad_based=*/true,
-            /*stop_history_eou_ms=*/800.0f});
+        VadEndpointer ep(
+            VadEndpointerCfg{/*enable=*/true, /*vad_based=*/true,
+                             /*stop_history_eou_ms=*/800.0f});
         // Speech up to 1000 ms, then silence. Poll every 100 ms.
         bool fired_before = false, fired_at = false, fired_after = false;
         for (double now = 1000.0; now < 1800.0; now += 100.0)  // 0..800 ms silence

tests/cpp/asr/test_postproc.cpp

@@ -82,9 +82,10 @@ main() {
     check(prof.mask("oh darn the heck") == "oh d*** the h***", "whole-word masking");
     // UTF-8 masking keeps the first codepoint and emits one '*' per remaining codepoint.
     check(
-        prof.mask("\xE0\xA4\xAF\xE0\xA4\xB9 "              // यह
-                  "\xE0\xA4\xA8\xE0\xA4\xA6\xE0\xA5\x80 "  // नदी
-                  "\xE0\xA4\xB9\xE0\xA5\x88") ==           // है
+        prof.mask(
+            "\xE0\xA4\xAF\xE0\xA4\xB9 "              // यह
+            "\xE0\xA4\xA8\xE0\xA4\xA6\xE0\xA5\x80 "  // नदी
+            "\xE0\xA4\xB9\xE0\xA5\x88") ==           // है
             "\xE0\xA4\xAF\xE0\xA4\xB9 \xE0\xA4\xA8** \xE0\xA4\xB9\xE0\xA5\x88",
         "UTF-8 (Devanagari) word masked per codepoint, valid UTF-8");
     check(prof.mask("darn, it") == "d***, it", "trailing punctuation preserved");

tests/cpp/tts/test_grpc_tts_config.cpp

@@ -88,8 +88,9 @@ main() {
         source_path("models/magpie_tts_multilingual_357m/magpie_tts_multilingual_357m.f16.gguf"));
     const std::string codec_model = env_or_default(
         "NEMO_SPEECH_TEST_TTS_CODEC_MODEL",
-        source_path("models/nemo_nano_codec_22khz_1.89kbps_21.5fps/"
-                    "nemo_nano_codec_22khz_1.89kbps_21.5fps.decoder.f16.gguf"));
+        source_path(
+            "models/nemo_nano_codec_22khz_1.89kbps_21.5fps/"
+            "nemo_nano_codec_22khz_1.89kbps_21.5fps.decoder.f16.gguf"));
     const std::string tokenizer_dir = env_or_default(
         "NEMO_SPEECH_TEST_TTS_TOKENIZER_DIR",
         source_path("models/magpie_tts_multilingual_357m/extracted"));