From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/models/sense_voice/whisper_lib/decoding.py | 117 +++++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 110 insertions(+), 7 deletions(-)
diff --git a/funasr/models/sense_voice/whisper_lib/decoding.py b/funasr/models/sense_voice/whisper_lib/decoding.py
index 49485d0..203efe8 100644
--- a/funasr/models/sense_voice/whisper_lib/decoding.py
+++ b/funasr/models/sense_voice/whisper_lib/decoding.py
@@ -10,6 +10,8 @@
from .audio import CHUNK_LENGTH
from .tokenizer import Tokenizer, get_tokenizer
from .utils import compression_ratio
+from funasr.models.transformer.utils.nets_utils import to_device
+
if TYPE_CHECKING:
from .model import Whisper
@@ -17,7 +19,7 @@
@torch.no_grad()
def detect_language(
- model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None
+ model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None, initial_prompt = None, x = None,
) -> Tuple[Tensor, List[dict]]:
"""
Detect the spoken language in the audio, and return them as list of strings, along with the ids
@@ -48,24 +50,34 @@
mel = mel.unsqueeze(0)
# skip encoder forward pass if already-encoded audio features were given
- if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+ # FIX(funasr): sense vocie
+ if mel.shape[-1] != model.dims.n_audio_state:
mel = model.encoder(mel)
# forward pass using a single token, startoftranscript
n_audio = mel.shape[0]
- x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]
- logits = model.logits(x, mel)[:, 0]
+ # FIX(funasr): sense vocie
+ # x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1]
+ if x is None:
+ x = torch.tensor([tokenizer.encode(initial_prompt, allowed_special="all")] * n_audio).to(mel.device) # [n_audio, 1]
+ else:
+ x = x.to(mel.device)
+
+ logits = model.logits(x[:,:-1], mel)[:, -1]
# collect detected languages; suppress all non-language tokens
mask = torch.ones(logits.shape[-1], dtype=torch.bool)
mask[list(tokenizer.all_language_tokens)] = False
+ mask[tokenizer.no_speech] = False
+
logits[:, mask] = -np.inf
language_tokens = logits.argmax(dim=-1)
language_token_probs = logits.softmax(dim=-1).cpu()
+
language_probs = [
{
c: language_token_probs[i, j].item()
- for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+ for j, c in zip(list(tokenizer.all_language_tokens) + [tokenizer.no_speech], list(tokenizer.all_language_codes) + ["nospeech"])
}
for i in range(n_audio)
]
@@ -106,12 +118,26 @@
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]]] = "<|Speech|><|BGM|><|Applause|><|Laughter|>"
+ gain_tokens_ed: Optional[Union[str, List[int]]] = "<|/Speech|><|/BGM|><|/Applause|><|/Laughter|>"
+ gain_tokens_score: List[float] = field(default_factory=lambda: [1, 1, 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
# implementation details
fp16: bool = True # use fp16 for most of the calculation
+
+ # FIX(funasr): sense vocie
+ initial_prompt: str = None
+ vocab_path: str = None
@dataclass(frozen=True)
@@ -437,6 +463,48 @@
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__(
@@ -520,6 +588,7 @@
num_languages=model.num_languages,
language=language,
task=options.task,
+ vocab_path=options.vocab_path
)
self.tokenizer: Tokenizer = tokenizer
self.options: DecodingOptions = self._verify_options(options)
@@ -556,6 +625,20 @@
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
@@ -609,6 +692,15 @@
+ prompt_tokens[-(self.n_ctx // 2 - 1) :]
+ tokens
)
+ #FIX(funasr): sense vocie
+ if initial_prompt := self.options.initial_prompt:
+ if self.options.language is not None:
+ initial_prompt = f"{initial_prompt}<|{self.options.language}|>"
+ tokens = self.tokenizer.encode(initial_prompt, allowed_special="all")
+ else:
+ tokens = self.tokenizer.encode(initial_prompt, allowed_special="all")
+ tokens += [0]
+
return tuple(tokens)
@@ -669,11 +761,22 @@
if self.options.language is None or self.options.task == "lang_id":
lang_tokens, lang_probs = self.model.detect_language(
- audio_features, self.tokenizer
+ audio_features, self.tokenizer, x=tokens
)
languages = [max(probs, key=probs.get) for probs in lang_probs]
+ # FIX(funasr): sense vocie
+ # if self.options.language is None:
+ # tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
if self.options.language is None:
- tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
+ # tokens[:, self.sot_index + 1] = lang_tokens # write language tokens
+ languages = "".join([f"<|{language}|>" for language in languages])
+
+ n_audio = audio_features.shape[0]
+ lang_tokens = torch.tensor([self.tokenizer.encode(languages, allowed_special="all")] * n_audio).to(
+ audio_features.device) # [n_audio, 1]
+
+ tokens[:, -1:] = lang_tokens[:, :]
+ languages = [languages]
return languages, lang_probs
--
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