Enable ruff in the pre-commit hooks (#124)

* Enable ruff in the pre-commit hooks.

* Disable the old hooks.

* Install uv in the CI.

.github/actions/moshi_build/action.yml

@@ -26,3 +26,7 @@ runs:
     run: |
       source  env/bin/activate
       pre-commit install
+  - name: Install uv
+    uses: astral-sh/setup-uv@v6
+    with:
+      version: "0.8.13"

.pre-commit-config.yaml

@@ -1,4 +1,18 @@
 repos:
+  - repo: local
+    hooks:
+    - id: ruff
+      name: ruff
+      language: system
+      entry: bash -c 'uvx ruff check'
+      pass_filenames: false
+      always_run: true
+    - id: ruff-format
+      name: ruff format
+      language: system
+      entry: bash -c 'uvx ruff format --check'
+      pass_filenames: false
+      always_run: true
     # Get rid of Jupyter Notebook output because we don't want to keep it in Git
   - repo: https://github.com/kynan/nbstripout
     rev: 0.8.1
@@ -9,14 +23,3 @@ repos:
     hooks:
       - id: check-added-large-files
         args: ["--maxkb=2048"]
-  - repo: https://github.com/astral-sh/ruff-pre-commit
-    # Ruff version.
-    rev: v0.11.7
-    hooks:
-      # Run the linter.
-      - id: ruff
-        types_or: [python, pyi] # Don't run on `jupyter` files
-        args: [--fix]
-      # Run the formatter.
-      - id: ruff-format
-        types_or: [python, pyi] # Don't run on `jupyter` files

scripts/tts_pytorch.py

@@ -117,16 +117,22 @@ def main():
                     break
                 time.sleep(1)
     else:
+        last_time = time.time()
 
         def _on_frame(frame):
             nonlocal _frames_cnt
+            nonlocal last_time
             if (frame != -1).all():
                 _frames_cnt += 1
                 print(f"generated {_frames_cnt / 12.5:.2f}s", end="\r", flush=True)
+            print("{}", time.time() - last_time)
+            last_time = time.time()
 
+        start_time = time.time()
         result = tts_model.generate(
             [entries], [condition_attributes], on_frame=_on_frame
         )
+        print(f"\nTotal time: {time.time() - start_time:.2f}s")
         with tts_model.mimi.streaming(1), torch.no_grad():
             pcms = []
             for frame in result.frames[tts_model.delay_steps :]:

stt_pytorch.ipynb

@@ -80,7 +80,6 @@
     "        self.lm_gen.streaming_forever(batch_size)\n",
     "\n",
     "    def run(self, in_pcms: torch.Tensor):\n",
-    "        device = self.lm_gen.lm_model.device\n",
     "        ntokens = 0\n",
     "        first_frame = True\n",
     "        chunks = [\n",

tts_pytorch.ipynb

@@ -21,9 +21,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import argparse\n",
-    "import sys\n",
-    "\n",
     "import numpy as np\n",
     "import torch\n",
     "from moshi.models.loaders import CheckpointInfo\n",
@@ -64,9 +61,7 @@
     "# CFG coef goes here because the model was trained with CFG distillation,\n",
     "# so it's not _actually_ doing CFG at inference time.\n",
     "# Also, if you are generating a dialog, you should have two voices in the list.\n",
-    "condition_attributes = tts_model.make_condition_attributes(\n",
+    "condition_attributes = tts_model.make_condition_attributes([voice_path], cfg_coef=2.0)"
-    "    [voice_path], cfg_coef=2.0\n",
-    ")"
    ]
   },
   {
@@ -79,17 +74,22 @@
     "print(\"Generating audio...\")\n",
     "\n",
     "pcms = []\n",
+    "\n",
+    "\n",
     "def _on_frame(frame):\n",
     "    print(\"Step\", len(pcms), end=\"\\r\")\n",
     "    if (frame != -1).all():\n",
     "        pcm = tts_model.mimi.decode(frame[:, 1:, :]).cpu().numpy()\n",
     "        pcms.append(np.clip(pcm[0, 0], -1, 1))\n",
     "\n",
+    "\n",
     "# You could also generate multiple audios at once by extending the following lists.\n",
     "all_entries = [entries]\n",
     "all_condition_attributes = [condition_attributes]\n",
     "with tts_model.mimi.streaming(len(all_entries)):\n",
-    "    result = tts_model.generate(all_entries, all_condition_attributes, on_frame=_on_frame)\n",
+    "    result = tts_model.generate(\n",
+    "        all_entries, all_condition_attributes, on_frame=_on_frame\n",
+    "    )\n",
     "\n",
     "print(\"Done generating.\")\n",
     "audio = np.concatenate(pcms, axis=-1)"
@@ -102,9 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "display(\n",
+    "display(Audio(audio, rate=tts_model.mimi.sample_rate, autoplay=True))"
-    "    Audio(audio, rate=tts_model.mimi.sample_rate, autoplay=True)\n",
-    ")"
    ]
   },
   {