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Merge d5328a849f4e599706e33efdbbca37cff875a649 into c0d2f624c09dc18e709e37c2ad90c039a4eb72a2

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Gaspard Petit 2025-06-26 14:06:02 +08:00 committed by GitHub
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whisper/decoding.py
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@ -18,18 +18,20 @@ if TYPE_CHECKING:
@torch.no_grad() @torch.no_grad()
def detect_language( def detect_language(
model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None
) -> Tuple[Tensor, List[dict]]: ) -> Tuple[Tensor, Union[Dict[str,float], List[Dict[str,float]]]]:
""" """
Detect the spoken language in the audio, and return them as list of strings, along with the ids Detect the spoken language in the audio, and return them as a list of strings, along with the ids
of the most probable language tokens and the probability distribution over all language tokens. of the most probable language tokens and the probability distribution over all language tokens.
This is performed outside the main decode loop in order to not interfere with kv-caching. This is performed outside the main decode loop in order to not interfere with kv-caching.
Returns Returns
------- -------
language_tokens : Tensor, shape = (n_audio,) language_tokens : Tensor, shape = (n_audio,)
ids of the most probable language tokens, which appears after the startoftranscript token. ids of the most probable language tokens, which appear after the startoftranscript token.
language_probs : List[Dict[str, float]], length = n_audio language_probs : Union[Dict[str, float], List[Dict[str, float]]]
list of dictionaries containing the probability distribution over all languages. If the input contains a single audio sample, this will be a dictionary containing the
probability distribution over all languages for that sample. If the input contains multiple
audio samples, this will be a list of such dictionaries, one for each sample (length = n_audio).
""" """
if tokenizer is None: if tokenizer is None:
tokenizer = get_tokenizer( tokenizer = get_tokenizer(