From 851e3e3ef83d0769d9bde172d8841f6b20e3e377 Mon Sep 17 00:00:00 2001
From: gaochangfeng <54253717+gaochangfeng@users.noreply.github.com>
Date: 星期三, 10 四月 2024 14:37:35 +0800
Subject: [PATCH] Gcf (#1605)
---
funasr/models/sense_voice/whisper_lib/decoding.py | 51 ++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 44 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..2239b64 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)
]
@@ -112,6 +124,10 @@
# 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)
@@ -520,6 +536,7 @@
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)
@@ -609,6 +626,15 @@
+ 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 +695,22 @@
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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