Dev gzf (#1422)

* fixbug

* qwenaudio

* qwenaudio whisper-openai v1.0.12

README.md

@@ -115,7 +115,7 @@ res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
                      hotword='魔搭')
 print(res)
 ```
-Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
+Note: `hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
 
 ### Speech Recognition (Streaming)
 ```python

README_zh.md

@@ -111,7 +111,7 @@ res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
             hotword='魔搭')
 print(res)
 ```
-注：`model_hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
+注：`hub`：表示模型仓库，`ms`为选择modelscope下载，`hf`为选择huggingface下载。
 
 ### 实时语音识别
 

examples/industrial_data_pretraining/whisper/demo_from_openai.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+# model = AutoModel(model="Whisper-small", hub="openai")
+# model = AutoModel(model="Whisper-medium", hub="openai")
+model = AutoModel(model="Whisper-large-v2", hub="openai")
+# model = AutoModel(model="Whisper-large-v3", hub="openai")
+
+res = model.generate(
+	language=None,
+	task="transcribe",
+	input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
+print(res)

examples/industrial_data_pretraining/whisper/infer_from_openai.sh

@@ -0,0 +1,24 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method1, inference from model hub
+
+# for more input type, please ref to readme.md
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"
+
+output_dir="./outputs/debug"
+
+#model="Whisper-small"
+#model="Whisper-medium"
+model="Whisper-large-v2"
+#model="Whisper-large-v3"
+hub="openai"
+
+device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"
+
+python -m funasr.bin.inference \
+++model=${model} \
+++hub=${hub} \
+++input="${input}" \
+++output_dir="${output_dir}" \
+++device="${device}" \

funasr/auto/auto_frontend.py

@@ -31,7 +31,7 @@ class AutoFrontend:
     def __init__(self, **kwargs):
         assert "model" in kwargs
         if "model_conf" not in kwargs:
-            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+            logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
             kwargs = download_model(**kwargs)
         
         # build frontend

funasr/auto/auto_model.py

@@ -143,7 +143,7 @@ class AutoModel:
     def build_model(self, **kwargs):
         assert "model" in kwargs
         if "model_conf" not in kwargs:
-            logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+            logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
             kwargs = download_model(**kwargs)
         
         set_all_random_seed(kwargs.get("seed", 0))
@@ -180,7 +180,7 @@ class AutoModel:
         
         # build model
         model_class = tables.model_classes.get(kwargs["model"])
-        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)
+        model = model_class(**kwargs, **kwargs.get("model_conf", {}), vocab_size=vocab_size)
         model.to(device)
         
         # init_param

funasr/bin/compute_audio_cmvn.py

@@ -18,7 +18,7 @@ def main_hydra(kwargs: DictConfig):
 
     assert "model" in kwargs
     if "model_conf" not in kwargs:
-        logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+        logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
         kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
     
 

funasr/bin/train.py

@@ -35,7 +35,7 @@ def main_hydra(kwargs: DictConfig):
 
     assert "model" in kwargs
     if "model_conf" not in kwargs:
-        logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+        logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
         kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
     
 

funasr/download/download_from_hub.py

@@ -2,13 +2,20 @@ import os
 import json
 from omegaconf import OmegaConf
 
-from funasr.download.name_maps_from_hub import name_maps_ms, name_maps_hf
+from funasr.download.name_maps_from_hub import name_maps_ms, name_maps_hf, name_maps_openai
 
 
 def download_model(**kwargs):
-    model_hub = kwargs.get("model_hub", "ms")
-    if model_hub == "ms":
+    hub = kwargs.get("hub", "ms")
+    if hub == "ms":
         kwargs = download_from_ms(**kwargs)
+    elif hub == "hf":
+        pass
+    elif hub == "openai":
+        model_or_path = kwargs.get("model")
+        if model_or_path in name_maps_openai:
+            model_or_path = name_maps_openai[model_or_path]
+        kwargs["model_path"] = model_or_path
     
     return kwargs
 
@@ -18,7 +25,13 @@ def download_from_ms(**kwargs):
         model_or_path = name_maps_ms[model_or_path]
     model_revision = kwargs.get("model_revision")
     if not os.path.exists(model_or_path):
-        model_or_path = get_or_download_model_dir(model_or_path, model_revision, is_training=kwargs.get("is_training"), check_latest=kwargs.get("check_latest", True))
+        try:
+            model_or_path = get_or_download_model_dir(model_or_path, model_revision,
+                                                      is_training=kwargs.get("is_training"),
+                                                      check_latest=kwargs.get("check_latest", True))
+        except Exception as e:
+            print(f"Download: {model_or_path} failed!: {e}")
+    
     kwargs["model_path"] = model_or_path
     
     if os.path.exists(os.path.join(model_or_path, "configuration.json")):
@@ -50,7 +63,9 @@ def download_from_ms(**kwargs):
             kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
         if os.path.exists(os.path.join(model_or_path, "jieba_usr_dict")):
             kwargs["jieba_usr_dict"] = os.path.join(model_or_path, "jieba_usr_dict")
-    return OmegaConf.to_container(kwargs, resolve=True)
+    if isinstance(kwargs, OmegaConf):
+        kwargs = OmegaConf.to_container(kwargs, resolve=True)
+    return kwargs
 
 def add_file_root_path(model_or_path: str, file_path_metas: dict, cfg = {}):
     

funasr/download/name_maps_from_hub.py

@@ -13,4 +13,19 @@ name_maps_ms = {
 
 name_maps_hf = {
 
+}
+
+name_maps_openai = {
+    "Whisper-tiny.en": "tiny.en",
+    "Whisper-tiny": "tiny",
+    "Whisper-base.en": "base.en",
+    "Whisper-base": "base",
+    "Whisper-small.en": "small.en",
+    "Whisper-small": "small",
+    "Whisper-medium.en": "medium.en",
+    "Whisper-medium": "medium",
+    "Whisper-large-v1": "large-v1",
+    "Whisper-large-v2": "large-v2",
+    "Whisper-large-v3": "large-v3",
+    "Whisper-large": "large",
 }

funasr/models/qwen_audio/model.py

@@ -14,7 +14,7 @@ from funasr.register import tables
 
 
 
-@tables.register("model_classes", "WhisperWarp")
+@tables.register("model_classes", "QwenAudioWarp")
 class WhisperWarp(nn.Module):
     def __init__(self, whisper_dims: dict, **kwargs):
         super().__init__()

funasr/models/whisper/model.py

@@ -13,16 +13,29 @@ from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
 from funasr.register import tables
 
 
-
-@tables.register("model_classes", "WhisperWarp")
+@tables.register("model_classes", "Whisper-tiny.en")
+@tables.register("model_classes", "Whisper-tiny")
+@tables.register("model_classes", "Whisper-base.en")
+@tables.register("model_classes", "Whisper-base")
+@tables.register("model_classes", "Whisper-small.en")
+@tables.register("model_classes", "Whisper-small")
+@tables.register("model_classes", "Whisper-medium.en")
+@tables.register("model_classes", "Whisper-medium")
+@tables.register("model_classes", "Whisper-large-v1")
+@tables.register("model_classes", "Whisper-large-v2")
+@tables.register("model_classes", "Whisper-large-v3")
+@tables.register("model_classes", "Whisper-WhisperWarp")
 class WhisperWarp(nn.Module):
-    def __init__(self, whisper_dims: dict, **kwargs):
+    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)
+            model_or_path = kwargs.get("model_path", "Whisper-large-v3")
+            if model_or_path.startswith("Whisper-"):
+                model_or_path = model_or_path.replace("Whisper-", "")
+            model = whisper.load_model(model_or_path)
         else:
+            whisper_dims = kwargs.get("whisper_dims", {})
             dims = whisper.model.ModelDimensions(**whisper_dims)
             model = whisper.model.Whisper(dims=dims)
         

funasr/version.txt

@@ -1 +1 @@
-1.0.11
+1.0.12