feat: solve some type hints issues

Author: Equipo45

faster_whisper/transcribe.py

@@ -7,7 +7,7 @@
 from dataclasses import asdict, dataclass
 from inspect import signature
 from math import ceil
-from typing import BinaryIO, Iterable, List, Optional, Tuple, Union
+from typing import Any, BinaryIO, Iterable, List, Optional, Tuple, Union
 from warnings import warn
 
 import ctranslate2
@@ -81,11 +81,11 @@ class TranscriptionOptions:
     compression_ratio_threshold: Optional[float]
     condition_on_previous_text: bool
     prompt_reset_on_temperature: float
-    temperatures: List[float]
+    temperatures: Union[List[float], Tuple[float, ...]]
     initial_prompt: Optional[Union[str, Iterable[int]]]
     prefix: Optional[str]
     suppress_blank: bool
-    suppress_tokens: Optional[List[int]]
+    suppress_tokens: Union[List[int], Tuple[int, ...]]
     without_timestamps: bool
     max_initial_timestamp: float
     word_timestamps: bool
@@ -106,7 +106,7 @@ class TranscriptionInfo:
     duration_after_vad: float
     all_language_probs: Optional[List[Tuple[str, float]]]
     transcription_options: TranscriptionOptions
-    vad_options: VadOptions
+    vad_options: Optional[VadOptions]
 
 
 class BatchedInferencePipeline:
@@ -121,7 +121,6 @@ def forward(self, features, tokenizer, chunks_metadata, options):
         encoder_output, outputs = self.generate_segment_batched(
             features, tokenizer, options
         )
-
         segmented_outputs = []
         segment_sizes = []
         for chunk_metadata, output in zip(chunks_metadata, outputs):
@@ -130,8 +129,8 @@ def forward(self, features, tokenizer, chunks_metadata, options):
             segment_sizes.append(segment_size)
             (
                 subsegments,
-                seek,
-                single_timestamp_ending,
+                _,
+                _,
             ) = self.model._split_segments_by_timestamps(
                 tokenizer=tokenizer,
                 tokens=output["tokens"],
@@ -295,7 +294,7 @@ def transcribe(
         hallucination_silence_threshold: Optional[float] = None,
         batch_size: int = 8,
         hotwords: Optional[str] = None,
-        language_detection_threshold: Optional[float] = 0.5,
+        language_detection_threshold: float = 0.5,
         language_detection_segments: int = 1,
     ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
         """transcribe audio in chunks in batched fashion and return with language info.
@@ -595,7 +594,7 @@ def __init__(
         num_workers: int = 1,
         download_root: Optional[str] = None,
         local_files_only: bool = False,
-        files: dict = None,
+        files: Optional[dict] = None,
         **model_kwargs,
     ):
         """Initializes the Whisper model.
@@ -744,7 +743,7 @@ def transcribe(
         clip_timestamps: Union[str, List[float]] = "0",
         hallucination_silence_threshold: Optional[float] = None,
         hotwords: Optional[str] = None,
-        language_detection_threshold: Optional[float] = 0.5,
+        language_detection_threshold: float = 0.5,
         language_detection_segments: int = 1,
     ) -> Tuple[Iterable[Segment], TranscriptionInfo]:
         """Transcribes an input file.
@@ -846,7 +845,7 @@ def transcribe(
             elif isinstance(vad_parameters, dict):
                 vad_parameters = VadOptions(**vad_parameters)
             speech_chunks = get_speech_timestamps(audio, vad_parameters)
-            audio_chunks, chunks_metadata = collect_chunks(audio, speech_chunks)
+            audio_chunks, _ = collect_chunks(audio, speech_chunks)
             audio = np.concatenate(audio_chunks, axis=0)
             duration_after_vad = audio.shape[0] / sampling_rate
 
@@ -938,7 +937,7 @@ def transcribe(
             condition_on_previous_text=condition_on_previous_text,
             prompt_reset_on_temperature=prompt_reset_on_temperature,
             temperatures=(
-                temperature if isinstance(temperature, (list, tuple)) else [temperature]
+                temperature if isinstance(temperature, (List, Tuple)) else [temperature]
             ),
             initial_prompt=initial_prompt,
             prefix=prefix,
@@ -966,7 +965,8 @@ def transcribe(
 
         if speech_chunks:
             segments = restore_speech_timestamps(segments, speech_chunks, sampling_rate)
-
+        if isinstance(vad_parameters, dict):
+            vad_parameters = VadOptions(**vad_parameters)
         info = TranscriptionInfo(
             language=language,
             language_probability=language_probability,
@@ -987,7 +987,7 @@ def _split_segments_by_timestamps(
         segment_size: int,
         segment_duration: float,
         seek: int,
-    ) -> List[List[int]]:
+    ) -> Tuple[List[Any], int, bool]:
         current_segments = []
         single_timestamp_ending = (
             len(tokens) >= 2 and tokens[-2] < tokenizer.timestamp_begin <= tokens[-1]
@@ -1530,8 +1530,8 @@ def add_word_timestamps(
         num_frames: int,
         prepend_punctuations: str,
         append_punctuations: str,
-        last_speech_timestamp: float,
-    ) -> float:
+        last_speech_timestamp: Union[float, None],
+    ) -> Optional[float]:
         if len(segments) == 0:
             return
 
@@ -1678,9 +1678,11 @@ def find_alignment(
             text_indices = np.array([pair[0] for pair in alignments])
             time_indices = np.array([pair[1] for pair in alignments])
 
-            words, word_tokens = tokenizer.split_to_word_tokens(
-                text_token + [tokenizer.eot]
-            )
+            if isinstance(text_token, int):
+                tokens = [text_token] + [tokenizer.eot]
+            else:
+                tokens = text_token + [tokenizer.eot]
+            words, word_tokens = tokenizer.split_to_word_tokens(tokens)
             if len(word_tokens) <= 1:
                 # return on eot only
                 # >>> np.pad([], (1, 0))
@@ -1728,7 +1730,7 @@ def detect_language(
         audio: Optional[np.ndarray] = None,
         features: Optional[np.ndarray] = None,
         vad_filter: bool = False,
-        vad_parameters: Union[dict, VadOptions] = None,
+        vad_parameters: Optional[Union[dict, VadOptions]] = None,
         language_detection_segments: int = 1,
         language_detection_threshold: float = 0.5,
     ) -> Tuple[str, float, List[Tuple[str, float]]]:
@@ -1760,18 +1762,24 @@ def detect_language(
         if audio is not None:
             if vad_filter:
                 speech_chunks = get_speech_timestamps(audio, vad_parameters)
-                audio_chunks, chunks_metadata = collect_chunks(audio, speech_chunks)
+                audio_chunks, _ = collect_chunks(audio, speech_chunks)
                 audio = np.concatenate(audio_chunks, axis=0)
-
+                assert (
+                    audio is not None
+                ), "Audio have a problem while concatanating the audio_chunks; return None"
             audio = audio[
                 : language_detection_segments * self.feature_extractor.n_samples
             ]
             features = self.feature_extractor(audio)
-
+        assert (
+            features is not None
+        ), "No features extracted from audio file; return None"
         features = features[
             ..., : language_detection_segments * self.feature_extractor.nb_max_frames
         ]
-
+        assert (
+            features is not None
+        ), "No features extracted when detectting language in audio segments; return None"
         detected_language_info = {}
         for i in range(0, features.shape[-1], self.feature_extractor.nb_max_frames):
             encoder_output = self.encode(
@@ -1841,13 +1849,13 @@ def get_compression_ratio(text: str) -> float:
 
 def get_suppressed_tokens(
     tokenizer: Tokenizer,
-    suppress_tokens: Tuple[int],
-) -> Optional[List[int]]:
-    if -1 in suppress_tokens:
+    suppress_tokens: Optional[List[int]],
+) -> Tuple[int, ...]:
+    if suppress_tokens is None or len(suppress_tokens) == 0:
+        suppress_tokens = []  # interpret empty string as an empty list
+    elif -1 in suppress_tokens:
         suppress_tokens = [t for t in suppress_tokens if t >= 0]
         suppress_tokens.extend(tokenizer.non_speech_tokens)
-    elif suppress_tokens is None or len(suppress_tokens) == 0:
-        suppress_tokens = []  # interpret empty string as an empty list
     else:
         assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
 

faster_whisper/vad.py

@@ -3,7 +3,7 @@
 import os
 
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -44,7 +44,7 @@ class VadOptions:
 
 def get_speech_timestamps(
     audio: np.ndarray,
-    vad_options: Optional[VadOptions] = None,
+    vad_options: Optional[Union[dict, VadOptions]] = None,
     sampling_rate: int = 16000,
     **kwargs,
 ) -> List[dict]:
@@ -62,6 +62,9 @@ def get_speech_timestamps(
     if vad_options is None:
         vad_options = VadOptions(**kwargs)
 
+    if isinstance(vad_options, dict):
+        vad_options = VadOptions(**vad_options)
+
     threshold = vad_options.threshold
     neg_threshold = vad_options.neg_threshold
     min_speech_duration_ms = vad_options.min_speech_duration_ms