replace NamedTuple with dataclass (#1105)

* replace `NamedTuple` with `dataclass`

* add deprecation warnings

faster_whisper/transcribe.py

@@ -6,9 +6,11 @@ import random
 import zlib
 
 from collections import Counter, defaultdict
+from dataclasses import asdict, dataclass
 from inspect import signature
 from math import ceil
-from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
+from typing import BinaryIO, Iterable, List, Optional, Tuple, Union
+from warnings import warn
 
 import ctranslate2
 import numpy as np
@@ -30,14 +32,24 @@ from faster_whisper.vad import (
 )
 
 
-class Word(NamedTuple):
+@dataclass
+class Word:
     start: float
     end: float
     word: str
     probability: float
 
+    def _asdict(self):
+        warn(
+            "Word._asdict() method is deprecated, use dataclasses.asdict(Word) instead",
+            DeprecationWarning,
+            2,
+        )
+        return asdict(self)
 
-class Segment(NamedTuple):
+
+@dataclass
+class Segment:
     id: int
     seek: int
     start: float
@@ -50,9 +62,18 @@ class Segment(NamedTuple):
     words: Optional[List[Word]]
     temperature: Optional[float] = 1.0
 
+    def _asdict(self):
+        warn(
+            "Segment._asdict() method is deprecated, use dataclasses.asdict(Segment) instead",
+            DeprecationWarning,
+            2,
+        )
+        return asdict(self)
+
 
 # Added additional parameters for multilingual videos and fixes below
-class TranscriptionOptions(NamedTuple):
+@dataclass
+class TranscriptionOptions:
     beam_size: int
     best_of: int
     patience: float
@@ -83,7 +104,8 @@ class TranscriptionOptions(NamedTuple):
     hotwords: Optional[str]
 
 
-class TranscriptionInfo(NamedTuple):
+@dataclass
+class TranscriptionInfo:
     language: str
     language_probability: float
     duration: float
@@ -108,7 +130,7 @@ class BatchedInferencePipeline:
     def __init__(
         self,
         model,
-        options: Optional[NamedTuple] = None,
+        options: Optional[TranscriptionOptions] = None,
         tokenizer=None,
         language: Optional[str] = None,
     ):
@@ -473,7 +495,7 @@ class BatchedInferencePipeline:
             results = self.forward(
                 features[i : i + batch_size],
                 chunks_metadata[i : i + batch_size],
-                **options._asdict(),
+                **asdict(options),
             )
 
             for result in results:
@@ -1043,16 +1065,15 @@ class WhisperModel:
         content_duration = float(content_frames * self.feature_extractor.time_per_frame)
 
         if isinstance(options.clip_timestamps, str):
-            options = options._replace(
+            options.clip_timestamps = [
-                clip_timestamps=[
+                float(ts)
-                    float(ts)
+                for ts in (
-                    for ts in (
+                    options.clip_timestamps.split(",")
-                        options.clip_timestamps.split(",")
+                    if options.clip_timestamps
-                        if options.clip_timestamps
+                    else []
-                        else []
+                )
-                    )
+            ]
-                ]
+
-            )
         seek_points: List[int] = [
             round(ts * self.frames_per_second) for ts in options.clip_timestamps
         ]
@@ -1999,23 +2020,17 @@ def restore_speech_timestamps(
                 # Ensure the word start and end times are resolved to the same chunk.
                 middle = (word.start + word.end) / 2
                 chunk_index = ts_map.get_chunk_index(middle)
-                word = word._replace(
+                word.start = ts_map.get_original_time(word.start, chunk_index)
-                    start=ts_map.get_original_time(word.start, chunk_index),
+                word.end = ts_map.get_original_time(word.end, chunk_index)
-                    end=ts_map.get_original_time(word.end, chunk_index),
-                )
                 words.append(word)
 
-            segment = segment._replace(
+            segment.start = words[0].start
-                start=words[0].start,
+            segment.end = words[-1].end
-                end=words[-1].end,
+            segment.words = words
-                words=words,
-            )
 
         else:
-            segment = segment._replace(
+            segment.start = ts_map.get_original_time(segment.start)
-                start=ts_map.get_original_time(segment.start),
+            segment.end = ts_map.get_original_time(segment.end)
-                end=ts_map.get_original_time(segment.end),
-            )
 
         yield segment
 

faster_whisper/vad.py

@@ -2,7 +2,8 @@ import bisect
 import functools
 import os
 
-from typing import Dict, List, NamedTuple, Optional, Tuple
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import torch
@@ -11,7 +12,8 @@ from faster_whisper.utils import get_assets_path
 
 
 # The code below is adapted from https://github.com/snakers4/silero-vad.
-class VadOptions(NamedTuple):
+@dataclass
+class VadOptions:
     """VAD options.
 
     Attributes: