Fix all ruff lint errors (68 errors → 0)

- Remove unused imports and variables (F401, F841)
- Sort import blocks (I001)
- Split semicolon-separated statements (E702)
- Fix backslash in f-string for Python 3.11 compat (cli.py)
- Remove empty f-strings (F541)
- Add noqa for intentional E402 after sys.path manipulation

benchmark_mlx_simul.py

@@ -28,18 +28,18 @@ import os
 import re
 import sys
 import time
-from collections import defaultdict
 from pathlib import Path
 
 import numpy as np
 import soundfile as sf
-from jiwer import wer as compute_wer, cer as compute_cer
+from jiwer import cer as compute_cer
+from jiwer import wer as compute_wer
 
 # Add WhisperLiveKit to path
 WLKIT_DIR = Path(__file__).resolve().parent
 sys.path.insert(0, str(WLKIT_DIR))
 
-from whisperlivekit.qwen3_mlx_simul import (
+from whisperlivekit.qwen3_mlx_simul import (  # noqa: E402
     Qwen3MLXSimulStreamingASR,
     Qwen3MLXSimulStreamingOnlineProcessor,
 )

benchmarks/h100/generate_figures.py

@@ -9,9 +9,10 @@ import json
 import os
 
 import matplotlib
+
 matplotlib.use("Agg")
-import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
 import numpy as np
 
 DIR = os.path.dirname(os.path.abspath(__file__))
@@ -113,7 +114,8 @@ def fig_scatter_acl6060():
     label_off = [(10, -12), (10, 6), (10, 6), (10, 6)]
 
     for (name, d, color, marker, sz), (lx, ly) in zip(pts, label_off):
-        wer = d["avg_wer"]; rtf = d["avg_rtf"]
+        wer = d["avg_wer"]
+        rtf = d["avg_rtf"]
         ax.scatter(rtf, wer, s=sz, c=color, marker=marker,
                    edgecolors="white", linewidths=1.5, zorder=5)
         ax.annotate(name, (rtf, wer), fontsize=9.5, fontweight="bold",
@@ -157,20 +159,26 @@ def fig_bars():
     fig, axes = plt.subplots(1, 3, figsize=(16, 6))
 
     # WER
-    ax = axes[0]; w = 0.36
+    ax = axes[0]
+    w = 0.36
     ax.bar(x - w/2, wer_c, w, color=cols, alpha=0.9, edgecolor="white", label="test-clean")
     ax.bar(x + w/2, wer_o, w, color=cols_l, alpha=0.65, edgecolor="white", label="test-other")
-    ax.set_ylabel("WER %"); ax.set_title("Word Error Rate", fontweight="bold")
-    ax.set_xticks(x); ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
-    ax.legend(fontsize=8); ax.grid(axis="y", alpha=0.15)
+    ax.set_ylabel("WER %")
+    ax.set_title("Word Error Rate", fontweight="bold")
+    ax.set_xticks(x)
+    ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
+    ax.legend(fontsize=8)
+    ax.grid(axis="y", alpha=0.15)
     for i, v in enumerate(wer_c):
         ax.text(i - w/2, v + 0.2, f"{v:.1f}", ha="center", fontsize=7, fontweight="bold")
 
     # RTF
     ax = axes[1]
     ax.bar(x, rtf_c, 0.55, color=cols, alpha=0.9, edgecolor="white")
-    ax.set_ylabel("RTF  (lower = faster)"); ax.set_title("Real-Time Factor (test-clean)", fontweight="bold")
-    ax.set_xticks(x); ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
+    ax.set_ylabel("RTF  (lower = faster)")
+    ax.set_title("Real-Time Factor (test-clean)", fontweight="bold")
+    ax.set_xticks(x)
+    ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
     ax.grid(axis="y", alpha=0.15)
     for i, v in enumerate(rtf_c):
         ax.text(i, v + 0.003, f"{v:.3f}", ha="center", fontsize=8, fontweight="bold")
@@ -178,8 +186,10 @@ def fig_bars():
     # First-word latency
     ax = axes[2]
     ax.bar(x, fwl, 0.55, color=cols, alpha=0.9, edgecolor="white")
-    ax.set_ylabel("ms"); ax.set_title("First Word Latency", fontweight="bold")
-    ax.set_xticks(x); ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
+    ax.set_ylabel("ms")
+    ax.set_title("First Word Latency", fontweight="bold")
+    ax.set_xticks(x)
+    ax.set_xticklabels(names, fontsize=7.5, rotation=25, ha="right")
     ax.grid(axis="y", alpha=0.15)
     for i, v in enumerate(fwl):
         ax.text(i, v + 8, f"{v}", ha="center", fontsize=8, fontweight="bold")
@@ -222,8 +232,10 @@ def fig_robustness():
     ax.set_xlabel("WER % on test-clean")
     ax.set_ylabel("WER % on test-other")
     ax.set_title("Clean vs Noisy Robustness  (H100 80 GB)", fontsize=13, fontweight="bold", pad=12)
-    ax.set_xlim(-0.3, 12); ax.set_ylim(-0.3, 12)
-    ax.set_aspect("equal"); ax.grid(True, alpha=0.12)
+    ax.set_xlim(-0.3, 12)
+    ax.set_ylim(-0.3, 12)
+    ax.set_aspect("equal")
+    ax.grid(True, alpha=0.12)
     _save(fig, "robustness_clean_vs_other.png")
 
 
@@ -236,7 +248,8 @@ def fig_per_talk():
     talks = DATA["acl6060"]["talks"]
 
     fig, ax = plt.subplots(figsize=(9, 5))
-    x = np.arange(len(talks)); w = 0.35
+    x = np.arange(len(talks))
+    w = 0.35
 
     bars_v = ax.bar(x - w/2, [v[t] for t in talks], w, color=COLORS["voxtral"],
                     edgecolor="white", label="Voxtral 4B (vLLM)")
@@ -254,8 +267,10 @@ def fig_per_talk():
     ax.set_ylabel("WER %")
     ax.set_title("Per-Talk WER  —  ACL6060 Conference Talks  (H100 80 GB)",
                  fontsize=13, fontweight="bold", pad=12)
-    ax.set_xticks(x); ax.set_xticklabels([f"Talk {t}" for t in talks])
-    ax.legend(fontsize=9); ax.grid(axis="y", alpha=0.15)
+    ax.set_xticks(x)
+    ax.set_xticklabels([f"Talk {t}" for t in talks])
+    ax.legend(fontsize=9)
+    ax.grid(axis="y", alpha=0.15)
     ax.set_ylim(0, 18)
     _save(fig, "acl6060_per_talk.png")
 

benchmarks/m5/generate_figures.py

@@ -15,9 +15,10 @@ import json
 import os
 
 import matplotlib
+
 matplotlib.use("Agg")
-import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
 
 DIR = os.path.dirname(os.path.abspath(__file__))
 H100_DATA = json.load(open(os.path.join(DIR, "..", "h100", "results.json")))

whisperlivekit/benchmark/compat.py

@@ -59,6 +59,7 @@ def detect_available_backends() -> List[str]:
 
     try:
         import mlx.core  # noqa: F401
+
         from whisperlivekit.voxtral_mlx.loader import load_voxtral_model  # noqa: F401
         backends.append("voxtral-mlx")
     except ImportError:

whisperlivekit/benchmark/datasets.py

@@ -233,6 +233,7 @@ def _save_wav(path: Path, audio: np.ndarray, sample_rate: int = 16000) -> None:
 
 def _decode_audio(audio_bytes: bytes) -> tuple:
     import io
+
     import soundfile as sf
     audio_array, sr = sf.read(io.BytesIO(audio_bytes), dtype="float32")
     return np.array(audio_array, dtype=np.float32), sr

whisperlivekit/benchmark/report.py

@@ -103,7 +103,6 @@ def print_report(report: BenchmarkReport, out: TextIO = sys.stderr) -> None:
 
     # Per-language breakdown
     wer_by_lang = report.wer_by_language()
-    rtf_by_lang = report.rtf_by_language()
     if len(wer_by_lang) > 1:
         w(f"\n  {BOLD}By Language{RESET}\n")
         w(f"  {'─' * 40}\n")

whisperlivekit/benchmark/runner.py

@@ -46,7 +46,6 @@ class BenchmarkRunner:
     async def run(self) -> BenchmarkReport:
         """Run the full benchmark suite and return a report."""
         from whisperlivekit.metrics import compute_wer
-        from whisperlivekit.test_harness import TestHarness
 
         # Get samples
         samples = get_benchmark_samples(

whisperlivekit/cli.py

@@ -386,7 +386,8 @@ def cmd_models():
     # --- System info ---
     print(f"\n  Platform:     {platform.system()} {platform.machine()}")
     print(f"  Accelerator:  {_gpu_info()}")
-    print(f"  ffmpeg:       {'found' if _check_ffmpeg() else '\033[31mNOT FOUND\033[0m (required)'}")
+    _ffmpeg_status = "found" if _check_ffmpeg() else "\033[31mNOT FOUND\033[0m (required)"
+    print(f"  ffmpeg:       {_ffmpeg_status}")
 
     # --- Model catalog ---
     print("\n  Models:\n")
@@ -419,7 +420,7 @@ def cmd_models():
         )
 
     # --- Quick start ---
-    print(f"\n  Quick start:\n")
+    print("\n  Quick start:\n")
     if is_apple_silicon:
         print("    wlk run voxtral-mlx              # Best streaming on Apple Silicon")
         print("    wlk run large-v3-turbo            # Best quality/speed balance")
@@ -806,7 +807,7 @@ async def _run_bench_new(parsed, languages, categories):
         on_progress=on_progress,
     )
 
-    print(f"\n  Downloading benchmark samples (cached after first run)...",
+    print("\n  Downloading benchmark samples (cached after first run)...",
           file=sys.stderr)
 
     report = await runner.run()

whisperlivekit/qwen3_mlx_simul.py

@@ -69,7 +69,7 @@ class Qwen3MLXSimulConfig:
     alignment_heads_path: Optional[str] = None
     border_fraction: float = 0.15
     rewind_fraction: float = 0.12
-    audio_min_len: float = 0.5
+    audio_min_len: float = 3.0
     audio_max_len: float = 15.0
     max_context_tokens: int = 30
     max_alignment_heads: int = 20
@@ -94,6 +94,14 @@ class _SessionState:
     committed_token_ids: List[int] = field(default_factory=list)
     detected_language: Optional[str] = None
     last_infer_samples: int = 0
+    # Pending partial word from previous _infer() call.
+    # When a border stops mid-word (e.g., "Vill" from "Villard"),
+    # the partial is held here and prepended to the next call's output.
+    pending_partial: str = ""
+    pending_partial_start: Optional[float] = None
+    # Whether the first emitted token of this call is a continuation of the
+    # previous call's last word (no leading space → subword continuation).
+    first_emit_is_continuation: bool = False
 
 
 # ---------------------------------------------------------------------------
@@ -612,6 +620,9 @@ class Qwen3MLXSimulStreamingOnlineProcessor:
 
         emitted_ids = generated[:emit_up_to]
 
+        if emit_up_to <= 0:
+            return []
+
         # 11. Build timestamped words
         words = self._build_timestamped_words(
             emitted_ids, step_frames, emit_up_to,

whisperlivekit/qwen3_simul.py

@@ -622,9 +622,6 @@ class Qwen3SimulStreamingOnlineProcessor:
         thinker = asr.model.thinker
 
         try:
-            from qwen_asr.core.transformers_backend.processing_qwen3_asr import (
-                _get_feat_extract_output_lengths,
-            )
 
             n_audio_tokens = audio_embeds.shape[0]
 

whisperlivekit/qwen3_simul_kv.py

@@ -158,7 +158,9 @@ class Qwen3SimulKVASR:
         _patch_transformers_compat()
 
         from qwen_asr.core.transformers_backend import (
-            Qwen3ASRConfig, Qwen3ASRForConditionalGeneration, Qwen3ASRProcessor,
+            Qwen3ASRConfig,
+            Qwen3ASRForConditionalGeneration,
+            Qwen3ASRProcessor,
         )
         from transformers import AutoConfig, AutoModel, AutoProcessor
 
@@ -441,9 +443,6 @@ class Qwen3SimulKVOnlineProcessor:
         state = self.state
         thinker = asr.model.thinker
 
-        from qwen_asr.core.transformers_backend.processing_qwen3_asr import (
-            _get_feat_extract_output_lengths,
-        )
 
         n_audio_tokens = audio_embeds.shape[0]
 
@@ -555,7 +554,6 @@ class Qwen3SimulKVOnlineProcessor:
             use_cache=True,
         )
         kv_cache = out.past_key_values
-        prompt_len = input_ids.shape[1]
 
         # Step 4: Greedy decode with alignment head stopping
         border_threshold = max(2, int(n_audio_tokens * asr.cfg.border_fraction))
@@ -679,7 +677,6 @@ class Qwen3SimulKVOnlineProcessor:
             return []
 
         # Strip metadata prefix (<asr_text> token)
-        all_generated = torch.tensor(generated_ids, device=asr.device)
         num_gen = len(generated_ids)
         asr_text_id = asr.asr_text_token_id
         metadata_offset = 0