drop python 3.7 support (openai#889)

abyesilyurt · Nov 13, 2023 · aead305 · aead305
1 parent 37bfedb
commit aead305
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Showing 3 changed files with 241 additions and 95 deletions.
diff --git a/whisper/decoding.py b/whisper/decoding.py
@@ -252,11 +252,10 @@ def __init__(self, temperature: float, eot: int):
         self.eot = eot
 
     def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
-        temperature = self.temperature
-        if temperature == 0:
+        if self.temperature == 0:
             next_tokens = logits.argmax(dim=-1)
         else:
-            next_tokens = Categorical(logits=logits / temperature).sample()
+            next_tokens = Categorical(logits=logits / self.temperature).sample()
 
         logprobs = F.log_softmax(logits.float(), dim=-1)
         current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
@@ -511,10 +510,8 @@ def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
 
     def _get_initial_tokens(self) -> Tuple[int]:
         tokens = list(self.sot_sequence)
-        prefix = self.options.prefix
-        prompt = self.options.prompt
 
-        if prefix:
+        if prefix := self.options.prefix:
             prefix_tokens = (
                 self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
             )
@@ -523,7 +520,7 @@ def _get_initial_tokens(self) -> Tuple[int]:
                 prefix_tokens = prefix_tokens[-max_prefix_len:]
             tokens = tokens + prefix_tokens
 
-        if prompt:
+        if prompt := self.options.prompt:
             prompt_tokens = (
                 self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
             )
@@ -698,13 +695,9 @@ def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOpt
     result: Union[DecodingResult, List[DecodingResult]]
         The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
     """
-    single = mel.ndim == 2
-    if single:
+    if single := mel.ndim == 2:
         mel = mel.unsqueeze(0)
 
     result = DecodingTask(model, options).run(mel)
-
-    if single:
-        result = result[0]
 
-    return result
+    return result[0] if single else result
diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py
@@ -1,6 +1,6 @@
 import os
 from dataclasses import dataclass
-from functools import lru_cache
+from functools import lru_cache, cached_property
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -156,43 +156,35 @@ def decode_with_timestamps(self, tokens) -> str:
         outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
         return "".join(outputs)
 
-    @property
-    @lru_cache()
+    @cached_property
     def eot(self) -> int:
         return self.tokenizer.eos_token_id
 
-    @property
-    @lru_cache()
+    @cached_property
     def sot(self) -> int:
         return self._get_single_token_id("<|startoftranscript|>")
 
-    @property
-    @lru_cache()
+    @cached_property
     def sot_lm(self) -> int:
         return self._get_single_token_id("<|startoflm|>")
 
-    @property
-    @lru_cache()
+    @cached_property
     def sot_prev(self) -> int:
         return self._get_single_token_id("<|startofprev|>")
 
-    @property
-    @lru_cache()
+    @cached_property
     def no_speech(self) -> int:
         return self._get_single_token_id("<|nospeech|>")
 
-    @property
-    @lru_cache()
+    @cached_property
     def no_timestamps(self) -> int:
         return self._get_single_token_id("<|notimestamps|>")
 
-    @property
-    @lru_cache()
+    @cached_property
     def timestamp_begin(self) -> int:
         return self.tokenizer.all_special_ids[-1] + 1
 
-    @property
-    @lru_cache()
+    @cached_property
     def language_token(self) -> int:
         """Returns the token id corresponding to the value of the `language` field"""
         if self.language is None:
@@ -210,8 +202,7 @@ def language_token(self) -> int:
 
         raise KeyError(f"Language {self.language} not found in tokenizer.")
 
-    @property
-    @lru_cache()
+    @cached_property
     def all_language_tokens(self) -> Tuple[int]:
         result = []
         for token, token_id in zip(
@@ -222,18 +213,15 @@ def all_language_tokens(self) -> Tuple[int]:
                 result.append(token_id)
         return tuple(result)
 
-    @property
-    @lru_cache()
+    @cached_property
     def all_language_codes(self) -> Tuple[str]:
         return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
 
-    @property
-    @lru_cache()
+    @cached_property
     def sot_sequence_including_notimestamps(self) -> Tuple[int]:
         return tuple(list(self.sot_sequence) + [self.no_timestamps])
 
-    @property
-    @lru_cache()
+    @cached_property
     def non_speech_tokens(self) -> Tuple[int]:
         """
         Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech