add always_use_initial_prompt

whisper/transcribe.py

@@ -43,6 +43,7 @@ def transcribe(
  no_speech_threshold: Optional[float] = 0.6,
  condition_on_previous_text: bool = True,
  initial_prompt: Optional[str] = None,
+ always_use_initial_prompt: bool = False,
  word_timestamps: bool = False,
  prepend_punctuations: str = "\"'“¿([{-",
  append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
@@ -97,6 +98,11 @@ def transcribe(
  "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
  to make it more likely to predict those word correctly.
 
+ always_use_initial_prompt: bool
+ if True, the initial_prompt will be used to all windows, and condition_on_previous_text
+ will be ignored. Enabling this may make the text more consistent if the audio is long
+ and you set the initial_prompt properly.
+
  decode_options: dict
  Keyword arguments to construct `DecodingOptions` instances
 
@@ -223,7 +229,11 @@ def new_segment(
  segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
  mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
 
- decode_options["prompt"] = all_tokens[prompt_reset_since:]
+ if always_use_initial_prompt:
+ decode_options["prompt"] = initial_prompt_tokens
+ else:
+ decode_options["prompt"] = all_tokens[prompt_reset_since:]
+
  result: DecodingResult = decode_with_fallback(mel_segment)
  tokens = torch.tensor(result.tokens)
 
@@ -390,6 +400,7 @@ def cli():
  parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
  parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
  parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+ parser.add_argument("--always_use_initial_prompt", type=str2bool, default=False, help="if True, the initial_prompt will be used to all windows, and condition_on_previous_text will be ignored. Enabling this may make the text more consistent if if the audio is long and you set the initial_prompt properly.")
  parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
 
  parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")