update

开源模型 ChatGLM/ChatGLM.py

@@ -38,7 +38,7 @@ elif choose_load_method == 1:
     model_chatglm3, tokenizer_chatglm3 = load_model_chatglm3()
 
 elif choose_load_method == 2:
-    # 在CPU上加载（需要25G内存，对话速度会比较慢）
+    # 在CPU上加载（需要25G内存，对话速度会比较慢，不推荐）
     @st.cache_resource
     def load_model_chatglm3():
         from transformers import AutoModel, AutoTokenizer

开源语音模型 Bark/Bark.py

@@ -0,0 +1,62 @@
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/38502
+"""
+
+import streamlit as st
+st.set_page_config(
+    page_title="Chat",
+    layout='wide'
+)
+
+choose_load_method = 0
+
+if choose_load_method == 0:
+    # GPU加载（需要5G显存）
+    @st.cache_resource
+    def load_bark_model():
+        from transformers import AutoProcessor, AutoModel
+        processor = AutoProcessor.from_pretrained("suno/bark")
+        model = AutoModel.from_pretrained("suno/bark").to("cuda")
+        return model, processor
+    model, processor = load_bark_model()
+
+elif choose_load_method == 1:
+    # CPU加载（需要9G内存，运行速度慢，不推荐）
+    @st.cache_resource
+    def load_bark_model():
+        from transformers import AutoProcessor, AutoModel
+        processor = AutoProcessor.from_pretrained("suno/bark")
+        model = AutoModel.from_pretrained("suno/bark")
+        return model, processor
+    model, processor = load_bark_model()
+    
+prompt = st.chat_input("在这里输入您的命令")
+
+prompt_placeholder = st.empty()
+with prompt_placeholder.container():
+    with st.chat_message("user", avatar='user'):
+        pass
+
+if prompt:
+    with prompt_placeholder.container():
+        with st.chat_message("user", avatar='user'):
+            st.write(prompt)
+    st.write('正在转换中，请稍后。')
+
+    inputs = processor(
+        text=[prompt],
+        return_tensors="pt",
+    )
+    if choose_load_method == 0:
+        inputs = {key: value.to("cuda") for key, value in inputs.items()}
+
+    speech_values = model.generate(**inputs, do_sample=True)
+
+    import scipy
+    sampling_rate = 24_000
+    scipy.io.wavfile.write('./a.wav', rate=sampling_rate, data=speech_values.cpu().numpy().squeeze())
+
+    audio_file = open('./a.wav', 'rb')
+    audio_bytes = audio_file.read()
+    st.audio(audio_bytes, format='audio/wav')