Update README.md

README.md

@@ -1,3 +1,97 @@
+# Spirit LM Inference Gradio Demo
+
+Copy the github repo, build the spiritlm python package and put models in `checkpoints` folder before running the script. I would suggest to use conda environment for this.
+
+```python
+import gradio as gr
+from spiritlm.model.spiritlm_model import Spiritlm, OutputModality, GenerationInput, ContentType
+from transformers import GenerationConfig
+import torchaudio
+import torch
+import tempfile
+import os
+import numpy as np
+
+# Initialize the Spirit LM model with the modified class
+spirit_lm = Spiritlm("spirit-lm-base-7b")
+
+def generate_output(input_type, input_content_text, input_content_audio, output_modality, temperature, top_p, max_new_tokens, do_sample):
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+    )
+
+    if input_type == "text":
+        interleaved_inputs = [GenerationInput(content=input_content_text, content_type=ContentType.TEXT)]
+    elif input_type == "audio":
+        # Load audio file
+        waveform, sample_rate = torchaudio.load(input_content_audio)
+        interleaved_inputs = [GenerationInput(content=waveform.squeeze(0), content_type=ContentType.SPEECH)]
+    else:
+        raise ValueError("Invalid input type")
+
+    outputs = spirit_lm.generate(
+        interleaved_inputs=interleaved_inputs,
+        output_modality=OutputModality[output_modality.upper()],
+        generation_config=generation_config,
+    )
+
+    text_output = ""
+    audio_output = None
+
+    for output in outputs:
+        if output.content_type == ContentType.TEXT:
+            text_output = output.content
+        elif output.content_type == ContentType.SPEECH:
+            # Ensure output.content is a NumPy array
+            if isinstance(output.content, np.ndarray):
+                # Debugging: Print shape and dtype of the audio data
+                print("Audio data shape:", output.content.shape)
+                print("Audio data dtype:", output.content.dtype)
+
+                # Ensure the audio data is in the correct format
+                if len(output.content.shape) == 1:
+                    # Mono audio data
+                    audio_data = torch.from_numpy(output.content).unsqueeze(0)
+                else:
+                    # Stereo audio data
+                    audio_data = torch.from_numpy(output.content)
+
+                # Save the audio content to a temporary file
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
+                    torchaudio.save(temp_audio_file.name, audio_data, 16000)
+                    audio_output = temp_audio_file.name
+            else:
+                raise TypeError("Expected output.content to be a NumPy array, but got {}".format(type(output.content)))
+
+    return text_output, audio_output
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_output,
+    inputs=[
+        gr.Radio(["text", "audio"], label="Input Type"),
+        gr.Textbox(label="Input Content (Text)"),
+        gr.Audio(label="Input Content (Audio)", type="filepath"),
+        gr.Radio(["TEXT", "SPEECH", "ARBITRARY"], label="Output Modality"),
+        gr.Slider(0, 1, step=0.1, value=0.9, label="Temperature"),
+        gr.Slider(0, 1, step=0.05, value=0.95, label="Top P"),
+        gr.Slider(1, 800, step=1, value=500, label="Max New Tokens"),
+        gr.Checkbox(value=True, label="Do Sample"),
+    ],
+    outputs=[gr.Textbox(label="Generated Text"), gr.Audio(label="Generated Audio")],
+    title="Spirit LM WebUI Demo",
+    description="Demo for generating text or audio using the Spirit LM model.",
+)
+
+# Launch the interface
+iface.launch()
+
+```
+
+
 # Spirit LM Checkpoints
 
 ## Download Checkpoints