From 753d579531e102e0c05358883af5d5ace02004e1 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 05 三月 2024 17:58:35 +0800
Subject: [PATCH] Dev gzf (#1428)
---
funasr/models/qwen_audio/model.py | 137 ++++++++++++++++++++++++++++++---------------
1 files changed, 90 insertions(+), 47 deletions(-)
diff --git a/funasr/models/qwen_audio/model.py b/funasr/models/qwen_audio/model.py
index 805234b..3eba026 100644
--- a/funasr/models/qwen_audio/model.py
+++ b/funasr/models/qwen_audio/model.py
@@ -9,25 +9,84 @@
from torch import nn
import whisper
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation import GenerationConfig
from funasr.register import tables
-
-
+@tables.register("model_classes", "Qwen/Qwen-Audio")
+@tables.register("model_classes", "Qwen-Audio")
+@tables.register("model_classes", "Qwen/QwenAudio")
+@tables.register("model_classes", "QwenAudio")
@tables.register("model_classes", "QwenAudioWarp")
-class WhisperWarp(nn.Module):
- def __init__(self, whisper_dims: dict, **kwargs):
+class QwenAudioWarp(nn.Module):
+ def __init__(self, *args, **kwargs):
super().__init__()
- hub = kwargs.get("hub", "funasr")
- if hub == "openai":
- init_param_path = kwargs.get("init_param_path", "large-v3")
- model = whisper.load_model(init_param_path)
- else:
- dims = whisper.model.ModelDimensions(**whisper_dims)
- model = whisper.model.Whisper(dims=dims)
+
+ model_or_path = kwargs.get("model_path", "QwenAudio")
+ model = AutoModelForCausalLM.from_pretrained(model_or_path, device_map="cpu",
+ trust_remote_code=True)
+ tokenizer = AutoTokenizer.from_pretrained(model_or_path, trust_remote_code=True)
+
self.model = model
+ self.tokenizer = tokenizer
+ def forward(self, ):
+ pass
+
+ def inference(self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+ if kwargs.get("batch_size", 1) > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+
+
+ meta_data = {}
+ # meta_data["batch_data_time"] = -1
+
+ sp_prompt = "<|startoftranscription|><|en|><|transcribe|><|en|><|notimestamps|><|wo_itn|>"
+ query = f"<audio>{data_in[0]}</audio>{sp_prompt}"
+ audio_info = self.tokenizer.process_audio(query)
+ inputs = self.tokenizer(query, return_tensors='pt', audio_info=audio_info)
+ inputs = inputs.to(self.model.device)
+ pred = self.model.generate(**inputs, audio_info=audio_info)
+ response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False, audio_info=audio_info)
+
+ results = []
+ result_i = {"key": key[0], "text": response}
+
+ results.append(result_i)
+
+ return results, meta_data
+
+@tables.register("model_classes", "Qwen/Qwen-Audio-Chat")
+@tables.register("model_classes", "Qwen/QwenAudioChat")
+@tables.register("model_classes", "Qwen-Audio-Chat")
+@tables.register("model_classes", "QwenAudioChat")
+@tables.register("model_classes", "QwenAudioChatWarp")
+class QwenAudioChatWarp(nn.Module):
+ def __init__(self, *args, **kwargs):
+ super().__init__()
+
+ model_or_path = kwargs.get("model_path", "QwenAudio")
+ bf16 = kwargs.get("bf16", False)
+ fp16 = kwargs.get("fp16", False)
+ model = AutoModelForCausalLM.from_pretrained(model_or_path,
+ device_map="cpu",
+ bf16=bf16,
+ fp16=fp16,
+ trust_remote_code=True)
+ tokenizer = AutoTokenizer.from_pretrained(model_or_path, trust_remote_code=True)
+
+ self.model = model
+ self.tokenizer = tokenizer
+
def forward(self, ):
pass
@@ -41,45 +100,29 @@
):
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
-
+
+
meta_data = {}
- if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
- speech, speech_lengths = data_in, data_lengths
- if len(speech.shape) < 3:
- speech = speech[None, :, :]
- if speech_lengths is None:
- speech_lengths = speech.shape[1]
+
+ prompt = kwargs.get("prompt", "what does the person say?")
+ cache = kwargs.get("cache", {})
+ history = cache.get("history", None)
+ if data_in[0] is not None:
+ # 1st dialogue turn
+ query = self.tokenizer.from_list_format([
+ {'audio': data_in[0]}, # Either a local path or an url
+ {'text': prompt},
+ ])
else:
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
- data_type=kwargs.get("data_type", "sound"),
- tokenizer=tokenizer)
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=frontend)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
- lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
- meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
-
- speech = speech.to(device=kwargs["device"])[0, :, :]
- speech_lengths = speech_lengths.to(device=kwargs["device"])
-
- # detect the spoken language
- _, probs = self.model.detect_language(speech)
- print(f"Detected language: {max(probs, key=probs.get)}")
-
- # decode the audio
- options = whisper.DecodingOptions(language=kwargs.get("language", None), fp16=False)
- result = whisper.decode(self.model, speech, options)
+ query = prompt
+ response, history = self.model.chat(self.tokenizer, query=query, history=history)
+ cache["history"] = history
+ # print(response)
+ # The person says: "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel".
results = []
- result_i = {"key": key[0], "text": result.text}
-
+ result_i = {"key": key[0], "text": response}
+
results.append(result_i)
-
+
return results, meta_data
-
\ No newline at end of file
--
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