Update

app.py

@@ -1,7 +1,169 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# def greet(name):
+#     return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+import spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, pipeline, set_seed
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+repo_id = "j2moreno/test-model/saved_model"
+s
+model = AutoModelForCausalLM.from_pretrained(repo_id).to(device)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+# feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
+
+SEED = 42
+
+default_text = "Ask me about Leonardo Moreno"
+# examples = [
+#     [
+#         "Remember - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of five times.",
+#         "A male speaker with a low-pitched voice delivering his words at a fast pace in a small, confined space with a very clear audio and an animated tone."
+#     ],
+#     [
+#         "'This is the best time of my life, Bartley,' she said happily.",
+#         "A female speaker with a slightly low-pitched, quite monotone voice delivers her words at a slightly faster-than-average pace in a confined space with very clear audio.",
+#     ],
+#     [
+#         "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
+#         "A male speaker with a slightly high-pitched voice delivering his words at a slightly slow pace in a small, confined space with a touch of background noise and a quite monotone tone.",
+#     ],
+#     [
+#         "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
+#         "A male speaker with a low-pitched voice delivers his words at a fast pace and an animated tone, in a very spacious environment, accompanied by noticeable background noise.",
+#     ],
+# ]
+
+# def preprocess(text):
+#     text = number_normalizer(text).strip()
+#     text = text.replace("-", " ")
+#     if text[-1] not in punctuation:
+#         text = f"{text}."s
+    
+#     abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b'
+    
+#     def separate_abb(chunk):
+#         chunk = chunk.replace(".","")
+#         print(chunk)
+#         return " ".join(chunk)
+    
+#     abbreviations = re.findall(abbreviations_pattern, text)
+#     for abv in abbreviations:
+#         if abv in text:
+#             text = text.replace(abv, separate_abb(abv))
+#     return text
+
+@spaces.GPU
+def generate_response(text):
+    set_seed(SEED)
+
+    tokenized_prompt = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    # print(tokenized_prompt)
+
+    output_sequences = model.generate(**tokenized_prompt, max_length=1024, num_return_sequences=1)
+    decoded_output = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
+    # print(decoded_output)
+
+    return decoded_output
+
+iface = gr.Interface(fn=generate_response, inputs="text", outputs="text")
 iface.launch()
+
+# css = """
+#         #share-btn-container {
+#             display: flex;
+#             padding-left: 0.5rem !important;
+#             padding-right: 0.5rem !important;
+#             background-color: #000000;
+#             justify-content: center;
+#             align-items: center;
+#             border-radius: 9999px !important; 
+#             width: 13rem;
+#             margin-top: 10px;
+#             margin-left: auto;
+#             flex: unset !important;
+#         }
+#         #share-btn {
+#             all: initial;
+#             color: #ffffff;
+#             font-weight: 600;
+#             cursor: pointer;
+#             font-family: 'IBM Plex Sans', sans-serif;
+#             margin-left: 0.5rem !important;
+#             padding-top: 0.25rem !important;
+#             padding-bottom: 0.25rem !important;
+#             right:0;
+#         }
+#         #share-btn * {
+#             all: unset !important;
+#         }
+#         #share-btn-container div:nth-child(-n+2){
+#             width: auto !important;
+#             min-height: 0px !important;
+#         }
+#         #share-btn-container .wrap {
+#             display: none !important;
+#         }
+# """
+# with gr.Blocks(css=css) as block:
+#     gr.HTML(
+#         """
+#             <div style="text-align: center; max-width: 700px; margin: 0 auto;">
+#               <div
+#                 style="
+#                   display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
+#                 "
+#               >
+#                 <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
+#                   Parler-TTS 🗣️
+#                 </h1>
+#               </div>
+#             </div>
+#         """
+#     )
+#     gr.HTML(
+#         f"""
+#         <p><a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> is a training and inference library for
+#         high-fidelity text-to-speech (TTS) models. The model demonstrated here, <a href="https://huggingface.co/parler-tts/parler_tts_mini_v0.1"> Parler-TTS Mini v0.1</a>, 
+#         is the first iteration model trained using 10k hours of narrated audiobooks. It generates high-quality speech 
+#         with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p>
+
+#         <p>Tips for ensuring good generation:
+#         <ul>
+#             <li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li>
+#             <li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li>
+#             <li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li>
+#         </ul>
+#         </p>
+#         """
+#     )
+#     with gr.Row():
+#         with gr.Column():
+#             input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
+#             description = gr.Textbox(label="Description", lines=2, value="", elem_id="input_description")
+#             run_button = gr.Button("Generate Audio", variant="primary")
+#         with gr.Column():
+#             audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out")
+
+#     inputs = [input_text, description]
+#     outputs = [audio_out]
+#     gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
+#     run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
+#     gr.HTML(
+#         """
+#         <p>To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech.
+#         The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention
+#         and torch compile, that will improve the latency by 2-4x. If you want to find out more about how this model was trained and even fine-tune it yourself, check-out the 
+#         <a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> repository on GitHub.</p>
+        
+#         <p>The Parler-TTS codebase and its associated checkpoints are licensed under <a href='https://github.com/huggingface/parler-tts?tab=Apache-2.0-1-ov-file#readme'> Apache 2.0</a>.</p>
+#         """
+#     )
+
+# block.queue()
+# block.launch(share=True)