Update README.md

README.md

@@ -32,16 +32,90 @@ We expand the Semantic tokens experiment with WhisperVQ as a tokenizer for audio
 
 ## How to Get Started with the Model
 
+Try this model using [Google Colab Notebook](https://colab.research.google.com/drive/18IiwN0AzBZaox5o0iidXqWD1xKq11XbZ?usp=sharing).
+
 First, we need to convert the audio file to sound tokens
 
 ```python
-
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if not os.path.exists("whisper-vq-stoks-medium-en+pl-fixed.model"):
+    hf_hub_download(
+        repo_id="jan-hq/WhisperVQ",
+        filename="whisper-vq-stoks-medium-en+pl-fixed.model",
+        local_dir=".",
+    )
+vq_model = RQBottleneckTransformer.load_model(
+        "whisper-vq-stoks-medium-en+pl-fixed.model"
+    ).to(device)
+def audio_to_sound_tokens(audio_path, target_bandwidth=1.5, device=device):
+    vq_model.ensure_whisper(device)
+
+    wav, sr = torchaudio.load(audio_path)
+    if sr != 16000:
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+    with torch.no_grad():
+        codes = vq_model.encode_audio(wav.to(device))
+        codes = codes[0].cpu().tolist()
+
+    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
+    return f'<|sound_start|>{result}<|sound_end|>'
+
+def audio_to_sound_tokens_transcript(audio_path, target_bandwidth=1.5, device=device):
+    vq_model.ensure_whisper(device)
+
+    wav, sr = torchaudio.load(audio_path)
+    if sr != 16000:
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+    with torch.no_grad():
+        codes = vq_model.encode_audio(wav.to(device))
+        codes = codes[0].cpu().tolist()
+
+    result = ''.join(f'<|sound_{num:04d}|>' for num in codes)
+    return f'<|reserved_special_token_69|><|sound_start|>{result}<|sound_end|>'
 ```
 
 Then, we can inference the model the same as any other LLM.
 
 ```python
-
+def setup_pipeline(model_path, use_4bit=False, use_8bit=False):
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+    model_kwargs = {"device_map": "auto"}
+
+    if use_4bit:
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+        )
+    elif use_8bit:
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(
+            load_in_8bit=True,
+            bnb_8bit_compute_dtype=torch.bfloat16,
+            bnb_8bit_use_double_quant=True,
+        )
+    else:
+        model_kwargs["torch_dtype"] = torch.bfloat16
+
+    model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
+
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def generate_text(pipe, messages, max_new_tokens=64, temperature=0.0, do_sample=False):
+    generation_args = {
+        "max_new_tokens": max_new_tokens,
+        "return_full_text": False,
+        "temperature": temperature,
+        "do_sample": do_sample,
+    }
+
+    output = pipe(messages, **generation_args)
+    return output[0]['generated_text']
+
+# Usage
+llm_path = "homebrewltd/llama3.1-s-instruct-v0.2"
+pipe = setup_pipeline(llm_path, use_8bit=True)
 ```
 
 ## Training process