Mikunono commited on
Commit
27a0fd9
1 Parent(s): 0e03caf

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +25 -24
app.py CHANGED
@@ -2,30 +2,6 @@ import gradio as gr
2
  from transformers import pipeline
3
  import librosa
4
 
5
-
6
- ########################ASR model###############################
7
-
8
- from transformers import WhisperProcessor, WhisperForConditionalGeneration
9
-
10
- # load model and processor
11
- processor = WhisperProcessor.from_pretrained("openai/whisper-base")
12
- model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
13
- model.config.forced_decoder_ids = None
14
-
15
- sample_rate = 16000
16
-
17
- def ASR_model(audio, sr=16000):
18
- DB_audio = audio
19
- input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features
20
- # generate token ids
21
- predicted_ids = model.generate(input_features)
22
- # decode token ids to text
23
- transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
24
-
25
- transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
26
-
27
- return transcription
28
-
29
  ########################LLama model###############################
30
  from transformers import AutoModelForCausalLM, AutoTokenizer
31
 
@@ -69,6 +45,31 @@ def RallyRespone(chat_history, message):
69
  res = t_chat[t_chat.rfind("Rally: "):]
70
  return res
71
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
72
  ########################Gradio UI###############################
73
 
74
  # Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
 
2
  from transformers import pipeline
3
  import librosa
4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5
  ########################LLama model###############################
6
  from transformers import AutoModelForCausalLM, AutoTokenizer
7
 
 
45
  res = t_chat[t_chat.rfind("Rally: "):]
46
  return res
47
 
48
+ ########################ASR model###############################
49
+
50
+ from transformers import WhisperProcessor, WhisperForConditionalGeneration
51
+
52
+ # load model and processor
53
+ processor = WhisperProcessor.from_pretrained("openai/whisper-base")
54
+ model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
55
+ model.config.forced_decoder_ids = None
56
+
57
+ sample_rate = 16000
58
+
59
+ def ASR_model(audio, sr=16000):
60
+ DB_audio = audio
61
+ input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features
62
+ # generate token ids
63
+ predicted_ids = model.generate(input_features)
64
+ # decode token ids to text
65
+ transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
66
+
67
+ transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
68
+
69
+ return transcription
70
+
71
+
72
+
73
  ########################Gradio UI###############################
74
 
75
  # Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.