Ruff lint cleanup

whisperlivekit/simul_whisper/align_att_base.py

@@ -1,7 +1,6 @@
 """Abstract base class for AlignAtt streaming decoders (PyTorch & MLX)."""
 import logging
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Tuple
 
 from whisperlivekit.timed_objects import ASRToken
 from whisperlivekit.whisper import DecodingOptions, tokenizer
@@ -151,7 +150,7 @@ class AlignAttBase(ABC):
             if seconds_since_start >= 2.0:
                 language_tokens, language_probs = self.lang_id(encoder_feature)
                 top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
-                print(f"Detected language: {top_lan} with p={p:.4f}")
+                logger.info(f"Detected language: {top_lan} with p={p:.4f}")
                 self.create_tokenizer(top_lan)
                 self.state.last_attend_frame = -self.cfg.rewind_threshold
                 self.state.cumulative_time_offset = 0.0

whisperlivekit/simul_whisper/backend.py

@@ -1,31 +1,27 @@
 import gc
 import logging
-import os
 import platform
 import sys
-from pathlib import Path
-from typing import List, Optional, Tuple
+from typing import List, Tuple
 
 import numpy as np
 import torch
 
-from whisperlivekit.backend_support import (faster_backend_available,
-                                            mlx_backend_available)
+from whisperlivekit.backend_support import faster_backend_available, mlx_backend_available
 from whisperlivekit.model_paths import detect_model_format, resolve_model_path
 from whisperlivekit.simul_whisper.config import AlignAttConfig
 from whisperlivekit.simul_whisper.simul_whisper import AlignAtt
 from whisperlivekit.timed_objects import ASRToken, ChangeSpeaker, Transcript
 from whisperlivekit.warmup import load_file
 from whisperlivekit.whisper import load_model, tokenizer
-from whisperlivekit.whisper.audio import TOKENS_PER_SECOND
 
 logger = logging.getLogger(__name__)
 
 
 HAS_MLX_WHISPER = mlx_backend_available(warn_on_missing=True)
 if HAS_MLX_WHISPER:
-    from .mlx_encoder import load_mlx_encoder, load_mlx_model, mlx_model_mapping
     from .mlx import MLXAlignAtt
+    from .mlx_encoder import load_mlx_encoder, load_mlx_model, mlx_model_mapping
 else:
     mlx_model_mapping = {}
     MLXAlignAtt = None
@@ -259,7 +255,7 @@ class SimulStreamingASR:
             self.mlx_encoder = load_mlx_encoder(path_or_hf_repo=mlx_model_path)
             self.shared_model = self.load_model()
         elif self.encoder_backend == "faster-whisper":
-            print('SimulStreaming will use Faster Whisper for the encoder.')
+            logger.info('SimulStreaming will use Faster Whisper for the encoder.')
             if self._resolved_model_path is not None:
                 fw_model = str(self._resolved_model_path)
             else:

whisperlivekit/simul_whisper/config.py

@@ -21,4 +21,3 @@ class AlignAttConfig():
     init_prompt: str = field(default=None)
     static_init_prompt: str = field(default=None)
     max_context_tokens: int = field(default=None)
-    

whisperlivekit/simul_whisper/decoder_state.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional, Tuple
+
 import torch
 
 

whisperlivekit/simul_whisper/mlx/simul_whisper.py

@@ -4,7 +4,6 @@ from typing import Any, List, Tuple
 
 import mlx.core as mx
 import numpy as np
-
 from mlx_whisper.audio import log_mel_spectrogram as mlx_log_mel_spectrogram
 from mlx_whisper.transcribe import pad_or_trim as mlx_pad_or_trim
 
@@ -15,7 +14,6 @@ from ..config import AlignAttConfig
 from .decoder_state import MLXDecoderState
 from .decoders import MLXBeamSearchDecoder, MLXGreedyDecoder, MLXInference
 
-
 logger = logging.getLogger(__name__)
 
 

whisperlivekit/simul_whisper/simul_whisper.py

@@ -6,13 +6,9 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 
-from whisperlivekit.backend_support import (faster_backend_available,
-                                            mlx_backend_available)
-from whisperlivekit.whisper.audio import (N_FRAMES, N_SAMPLES,
-                                          TOKENS_PER_SECOND,
-                                          log_mel_spectrogram, pad_or_trim)
-from whisperlivekit.whisper.decoding import (BeamSearchDecoder, GreedyDecoder,
-                                             SuppressTokens)
+from whisperlivekit.backend_support import faster_backend_available, mlx_backend_available
+from whisperlivekit.whisper.audio import N_FRAMES, N_SAMPLES, TOKENS_PER_SECOND, log_mel_spectrogram, pad_or_trim
+from whisperlivekit.whisper.decoding import BeamSearchDecoder, GreedyDecoder, SuppressTokens
 from whisperlivekit.whisper.timing import median_filter
 
 from .align_att_base import DEC_PAD, AlignAttBase
@@ -25,8 +21,7 @@ from .token_buffer import TokenBuffer
 logger = logging.getLogger(__name__)
 
 if mlx_backend_available():
-    from mlx_whisper.audio import \
-        log_mel_spectrogram as mlx_log_mel_spectrogram
+    from mlx_whisper.audio import log_mel_spectrogram as mlx_log_mel_spectrogram
     from mlx_whisper.transcribe import pad_or_trim as mlx_pad_or_trim
 
 if faster_backend_available():

whisperlivekit/simul_whisper/token_buffer.py

@@ -1,4 +1,3 @@
-import sys
 
 import torch
 

whisperlivekit/whisper/__init__.py

@@ -11,10 +11,8 @@ import torch
 from torch import Tensor
 from tqdm import tqdm
 
-from whisperlivekit.whisper.audio import (load_audio, log_mel_spectrogram,
-                                          pad_or_trim)
-from whisperlivekit.whisper.decoding import (DecodingOptions, DecodingResult,
-                                             decode, detect_language)
+from whisperlivekit.whisper.audio import load_audio, log_mel_spectrogram, pad_or_trim
+from whisperlivekit.whisper.decoding import DecodingOptions, DecodingResult, decode, detect_language
 from whisperlivekit.whisper.model import ModelDimensions, Whisper
 from whisperlivekit.whisper.transcribe import transcribe
 from whisperlivekit.whisper.version import __version__

whisperlivekit/whisper/decoding.py

@@ -1,6 +1,5 @@
 from dataclasses import dataclass, field, replace
-from typing import (TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence,
-                    Tuple, Union)
+from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch

whisperlivekit/whisper/transcribe.py

@@ -8,13 +8,11 @@ import numpy as np
 import torch
 import tqdm
 
-from .audio import (FRAMES_PER_SECOND, HOP_LENGTH, N_FRAMES, N_SAMPLES,
-                    SAMPLE_RATE, log_mel_spectrogram, pad_or_trim)
+from .audio import FRAMES_PER_SECOND, HOP_LENGTH, N_FRAMES, N_SAMPLES, SAMPLE_RATE, log_mel_spectrogram, pad_or_trim
 from .decoding import DecodingOptions, DecodingResult
 from .timing import add_word_timestamps
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
-from .utils import (exact_div, format_timestamp, get_end, get_writer,
-                    make_safe, optional_float, optional_int, str2bool)
+from .utils import exact_div, format_timestamp, get_end, get_writer, make_safe, optional_float, optional_int, str2bool
 
 if TYPE_CHECKING:
     from .model import Whisper

whisperlivekit/whisper/val.py

@@ -6,9 +6,10 @@ Everything else is just efficiency.
 @karpathy
 """
 
-import os       # os.path.exists
 import math  # math.log, math.exp
+import os  # os.path.exists
 import random  # random.seed, random.choices, random.gauss, random.shuffle
+
 random.seed(42) # Let there be order among chaos
 
 # Let there be an input dataset `docs`: list[str] of documents (e.g. a dataset of names)