Update README.md

README.md

@@ -36,4 +36,97 @@ datasets:
 - fdemelo/ipa-childes-split
 license: apache-2.0
 pipeline_tag: text-generation
----
+---
+
+onnx version of [fdemelo/g2p-multilingual-byt5-tiny-8l-ipa-childes](https://huggingface.co/fdemelo/g2p-multilingual-byt5-tiny-8l-ipa-childes)
+
+inference example
+
+```python
+from transformers import AutoTokenizer
+import onnxruntime
+import numpy as np
+
+
+def infer_onnx(text: str, lang: str, onnx_model_path: str = "byt5_g2p_model.onnx"):
+    """
+    Exports the ByT5 model to ONNX format and then performs inference using ONNX Runtime.
+
+    Args:
+        text (str): The input text to convert to phonemes.
+        lang (str): The language tag (e.g., "en").
+        onnx_model_path (str): The path to save/load the ONNX model.
+    """
+    model_name = 'fdemelo/g2p-multilingual-byt5-tiny-8l-ipa-childes'
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # --- Step 2: Perform Inference with ONNX Runtime ---
+    print("\n--- Performing inference with ONNX Runtime ---")
+
+    # Create an ONNX Runtime session
+    try:
+        session = onnxruntime.InferenceSession(onnx_model_path, providers=['CPUExecutionProvider'])
+    except Exception as e:
+        print(f"Error loading ONNX model: {e}")
+        return
+
+    # Get input and output names from the ONNX model
+    onnx_input_names = [inp.name for inp in session.get_inputs()]
+    onnx_output_names = [out.name for out in session.get_outputs()]
+
+    # Prepare actual input for ONNX inference
+    input_text_for_onnx = f"<{lang}>: {text}"
+    inputs_for_onnx = tokenizer([input_text_for_onnx], return_tensors="pt", add_special_tokens=False)
+
+    input_ids_np = inputs_for_onnx["input_ids"].cpu().numpy()
+    attention_mask_np = inputs_for_onnx["attention_mask"].cpu().numpy()
+
+    # Manual greedy decoding loop for ONNX Runtime
+    # This simulates the 'generate' method's greedy decoding.
+    generated_ids = []
+    # T5 models typically use pad_token_id as the initial token for generation
+    # or a specific decoder_start_token_id.
+    # For T5, the decoder_start_token_id is usually the pad_token_id.
+    current_decoder_input_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
+
+    # Ensure it's a batch of 1
+    decoder_input_ids_np = np.array([[current_decoder_input_id]])
+
+    max_length = 512  # Same as in the original predict_byt5
+
+    # Store encoder outputs if needed for cross-attention in decoder (T5 does this)
+    # When exporting the full T5 model's forward pass, the encoder_hidden_states
+    # are implicitly handled within the graph. We just need to feed the decoder_input_ids.
+
+    for _ in range(max_length):
+        # Prepare inputs for the current step
+        onnx_inputs = {
+            "input_ids": input_ids_np,
+            "attention_mask": attention_mask_np,
+            "decoder_input_ids": decoder_input_ids_np
+        }
+
+        # Run inference
+        outputs = session.run(onnx_output_names, onnx_inputs)
+        logits = outputs[0]  # Get the logits
+
+        # Get the logits for the last token in the sequence
+        next_token_logits = logits[0, -1, :]  # Batch 0, last token, all vocab logits
+
+        # Greedy decoding: pick the token with the highest logit
+        next_token_id = np.argmax(next_token_logits)
+        generated_ids.append(next_token_id)
+
+        # Check for end-of-sequence token
+        if next_token_id == tokenizer.eos_token_id:
+            break
+
+        # Update decoder input for the next step
+        # Append the new token to the decoder input sequence
+        decoder_input_ids_np = np.concatenate((decoder_input_ids_np, np.array([[next_token_id]])), axis=1)
+
+    # Decode the generated ONNX phoneme IDs
+    onnx_phones = tokenizer.batch_decode([generated_ids], skip_special_tokens=True)
+    print(f"ONNX Runtime Inference: {onnx_phones}")
+    return onnx_phones
+``