Add ONNX and Transformers Python quickstarts

README.md

@@ -53,6 +53,60 @@ For longer transcripts, the runtime uses 128-token windows with 12 words of over
 and keeps every input word in order. Pass continuous text when possible so PnC can
 use context across speech recognition chunks.
 
+### Python with ONNX
+
+Install `ekko-stt[pnc]` in your Python environment. This loads the int8 ONNX
+export and applies the same windowing as the command above:
+
+```python
+from ekko import Punctuator
+from ekko.artifacts import ensure_pnc_directory
+
+pnc = Punctuator(ensure_pnc_directory())
+print(pnc("hej mit navn er emil hvordan går det i dag"))
+```
+
+<details>
+<summary>Python with Transformers (PyTorch)</summary>
+
+Install `transformers` and `torch` to use the PyTorch weights directly. This
+short-input example maps predictions from the first token of each word back to
+the original words:
+
+```python
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+repo = "RyeAI/ekko-pnc"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForTokenClassification.from_pretrained(repo).eval()
+words = "hej mit navn er emil hvordan går det i dag".split()
+inputs = tokenizer(words, is_split_into_words=True, return_tensors="pt")
+
+with torch.inference_mode():
+    predictions = model(**inputs).logits.argmax(dim=-1)[0].tolist()
+
+output, seen, capitalize_next = [], set(), True
+for word_id, prediction in zip(inputs.word_ids(), predictions):
+    if word_id is None or word_id in seen:
+        continue
+    seen.add(word_id)
+    label = model.config.id2label[prediction]
+    mark = label.removesuffix("|U")
+    word = words[word_id]
+    if capitalize_next or label.endswith("|U"):
+        word = word[:1].upper() + word[1:]
+    output.append(word + ("" if mark == "O" else mark))
+    capitalize_next = mark in {".", "?", "!"}
+
+print(" ".join(output))
+```
+
+</details>
+
+For longer transcripts, use the ONNX example above; it preserves all words
+across overlapping windows.
+
 Version 2 is tuned for **continuous speech** such as dictation and meetings.
 The earlier version often missed sentence endings when given a long transcript;
 it had mainly learned from short, isolated utterances. Version 2 was trained