Mirroristas/whisper
2
0
Fork 0
mirror of https://github.com/openai/whisper.git synced 2025-11-24 14:35:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update transcribe.py

Browse Source
This commit is contained in:
Alexander Kuznetsov 2024-11-01 20:33:52 +03:00 committed by GitHub
parent 80ddd07c28
commit 482a5b89d8
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 18 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
whisper/transcribe.py
View File

@ -76,8 +76,8 @@ def transcribe(
compression_ratio_threshold: float
If the gzip compression ratio is above this value, treat as failed
compression_ratio_hallucination_threshold: float
If the gzip compression ratio is above this value after all attempts to decode, treat as a halucination and skip
compression_ratio_halcination_threshold: float
If the gzip compression ratio is above this value after all attempts to decode, treat as a hallucination and skip
logprob_threshold: float
If the average log probability over sampled tokens is below this value, treat as failed
@ -106,6 +106,11 @@ 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.
carry_initial_prompt: bool
If carry_initial_prompt is True, `initial_prompt` is prepended to the prompt of each internal
`decode()` call. If there is not enough context space at the start of the prompt, it is
left-sliced to make space.
decode_options: dict
Keyword arguments to construct `DecodingOptions` instances
@ -221,8 +226,6 @@ def transcribe(
):
# Discard the segment
continue # Skip to the next segment
if not needs_fallback:
break
@ -240,9 +243,11 @@ def transcribe(
all_segments = []
prompt_reset_since = 0
remaining_prompt_length = model.dims.n_text_ctx // 2 - 1
if initial_prompt is not None:
initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
all_tokens.extend(initial_prompt_tokens)
remaining_prompt_length -= len(initial_prompt_tokens)
else:
initial_prompt_tokens = []
@ -288,7 +293,13 @@ def transcribe(
segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
if carry_initial_prompt:
nignored = max(len(initial_prompt_tokens), prompt_reset_since)
remaining_prompt = all_tokens[nignored:][-remaining_prompt_length:]
decode_options["prompt"] = initial_prompt_tokens + remaining_prompt
else:
decode_options["prompt"] = all_tokens[prompt_reset_since:]
result: DecodingResult = decode_with_fallback(mel_segment)
tokens = torch.tensor(result.tokens)
@ -542,6 +553,8 @@ 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("--carry_initial_prompt", type=str2bool, default=False, help="if True, prepend initial_prompt to every internal decode() call. May reduce the effectiveness of condition_on_previous_text")
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("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")