| | |
|---|
| | | suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" |
|---|
| | | suppress_blank: bool = True # this will suppress blank outputs |
|---|
| | | |
|---|
| | | gain_event: bool = False # this will suppress blank outputs |
|---|
| | | gain_tokens_bg: Optional[Union[str, List[int]]] = "<|Applause|><|Laughter|>" |
|---|
| | | gain_tokens_ed: Optional[Union[str, List[int]]] = "<|/Applause|><|/Laughter|>" |
|---|
| | | gain_tokens_score: List[float] = field(default_factory=lambda: [25.0, 5.0]) #[25, 5] |
|---|
| | | |
|---|
| | | use_emo_threshold: bool = False # this will suppress blank outputs |
|---|
| | | emo_unk_token: Optional[Union[str, List[int]]] = "<|SPECIAL_TOKEN_1|>" |
|---|
| | | emo_target_tokens: Optional[Union[str, List[int]]] = "<|HAPPY|><|SAD|><|ANGRY|>" |
|---|
| | | emo_target_threshold: List[float] = field(default_factory=lambda: [0.1, 0.1, 0.1]) #[25, 5] |
|---|
| | | |
|---|
| | | # timestamp sampling options |
|---|
| | | without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only |
|---|
| | | max_initial_timestamp: Optional[float] = 1.0 |
|---|
| | |
|---|
| | | def apply(self, logits: Tensor, tokens: Tensor): |
|---|
| | | logits[:, self.suppress_tokens] = -np.inf |
|---|
| | | |
|---|
| | | class GainEventToken(LogitFilter): |
|---|
| | | def __init__(self, bg_tokens: Sequence[int], ed_tokens:Sequence[int], gain_values: Sequence[float]): |
|---|
| | | self.bg_tokens = list(bg_tokens) |
|---|
| | | self.ed_tokens = list(ed_tokens) |
|---|
| | | self.gain_value = [np.log(max(ga, 1e-9)) for ga in gain_values] |
|---|
| | | assert len(self.ed_tokens) == len(self.gain_value) |
|---|
| | | assert len(self.bg_tokens) == len(self.gain_value) |
|---|
| | | |
|---|
| | | def apply(self, logits: Tensor, tokens: Tensor): |
|---|
| | | for i in range(len(tokens)): |
|---|
| | | for bg, ed, ga in zip(self.bg_tokens, self.ed_tokens, self.gain_value): |
|---|
| | | sum_bg = sum([1 if x == bg else 0 for x in tokens[i]]) |
|---|
| | | sum_ed = sum([1 if x == ed else 0 for x in tokens[i]]) |
|---|
| | | logits[i, bg] += ga |
|---|
| | | if sum_bg > sum_ed or tokens[i,-1] in [bg, ed]: |
|---|
| | | logits[i, bg] = -np.inf |
|---|
| | | if sum_bg <= sum_ed: |
|---|
| | | logits[i, ed] = -np.inf |
|---|
| | | |
|---|
| | | class ThresholdEmoToken(LogitFilter): |
|---|
| | | def __init__(self, unk_tokens: Sequence[int], emo_tokens:Sequence[int], th_values: Sequence[float]): |
|---|
| | | self.unk_token = list(unk_tokens)[0] |
|---|
| | | self.emo_tokens = list(emo_tokens) |
|---|
| | | self.th_values = list(th_values) |
|---|
| | | assert len(self.emo_tokens) == len(self.th_values) |
|---|
| | | |
|---|
| | | def apply(self, logits: Tensor, tokens: Tensor): |
|---|
| | | for i in range(len(tokens)): |
|---|
| | | for emo, th in zip(self.emo_tokens, self.th_values): |
|---|
| | | if logits[i].argmax() == emo and logits[i].softmax(dim=-1)[emo] < th: |
|---|
| | | logits[i, self.unk_token] = max(logits[i, emo], logits[i, self.unk_token]) |
|---|
| | | logits[i, emo] = -np.inf |
|---|
| | | |
|---|
| | | # for bg, ed, ga in zip(self.bg_tokens, self.ed_tokens, self.gain_value): |
|---|
| | | # sum_bg = sum([1 if x == bg else 0 for x in tokens[i]]) |
|---|
| | | # sum_ed = sum([1 if x == ed else 0 for x in tokens[i]]) |
|---|
| | | # logits[i, bg] += ga |
|---|
| | | # if sum_bg > sum_ed or tokens[i,-1] in [bg, ed]: |
|---|
| | | # logits[i, bg] = -np.inf |
|---|
| | | # if sum_bg <= sum_ed: |
|---|
| | | # logits[i, ed] = -np.inf |
|---|
| | | |
|---|
| | | |
|---|
| | | class ApplyTimestampRules(LogitFilter): |
|---|
| | | def __init__( |
|---|
| | |
|---|
| | | self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) |
|---|
| | | if self.options.suppress_tokens: |
|---|
| | | self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) |
|---|
| | | if self.options.gain_event: |
|---|
| | | self.logit_filters.append(GainEventToken( |
|---|
| | | self.tokenizer.encode(self.options.gain_tokens_bg, allowed_special="all"), |
|---|
| | | self.tokenizer.encode(self.options.gain_tokens_ed, allowed_special="all"), |
|---|
| | | self.options.gain_tokens_score |
|---|
| | | ) |
|---|
| | | ) |
|---|
| | | if self.options.use_emo_threshold: |
|---|
| | | self.logit_filters.append(ThresholdEmoToken( |
|---|
| | | self.tokenizer.encode(self.options.emo_unk_token, allowed_special="all"), |
|---|
| | | self.tokenizer.encode(self.options.emo_target_tokens, allowed_special="all"), |
|---|
| | | self.options.emo_target_threshold |
|---|
| | | ) |
|---|
| | | ) |
|---|
| | | if not options.without_timestamps: |
|---|
| | | precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds |
|---|
| | | max_initial_timestamp_index = None |
|---|
