chore: import upstream snapshot with attribution

doc/source/serve/doc_code/whisper_example.py

@@ -0,0 +1,82 @@
+from ray import serve
+import re
+import subprocess
+from typing import List
+import starlette.requests
+
+
+def hard_normalize(word):
+    """Lower case the word and remove all non-alpha-numeric characters
+    from the entire word.
+    """
+
+    non_alpha_numeric = re.compile(r"[\W]+")
+    return non_alpha_numeric.sub("", word.lower())
+
+
+def clean_whisper_alignments(whisper_word_alignments: List[dict]) -> List[dict]:
+    """Change required to match gentle's tokenization with Whisper's word alignments"""
+
+    processed_words = []
+    for word_alignment in whisper_word_alignments:
+        if word_alignment.word == "%":
+            processed_words.append(word_alignment._replace(word=" percent"))
+        elif word_alignment.word[0] == "'" and len(processed_words) > 0:
+            # eg: "'Or" from ["d", "'Or"]
+            processed_words[-1]._replace(
+                word=processed_words[-1].word + word_alignment.word,
+                end=word_alignment.end,
+            )
+        elif hard_normalize(word_alignment.word) == "":
+            # eg: " -"
+            continue
+        else:
+            processed_words.append(word_alignment)
+
+    return processed_words
+
+
+@serve.deployment(ray_actor_options={"num_cpus": 1.0, "num_gpus": 1})
+class WhisperModel:
+    def __init__(self, model_size="large-v2"):
+        # Load model
+        from faster_whisper import WhisperModel
+
+        # Run on GPU with FP16
+        self.model = WhisperModel(model_size, device="cuda", compute_type="float16")
+
+    async def transcribe(self, file_path: str):
+        subprocess.check_call(["curl", "-o", "audio.mp3", "-sSfLO", file_path])
+
+        segments, info = self.model.transcribe(
+            "audio.mp3",
+            language="en",
+            initial_prompt="Here is the um, uh, Um, Uh, transcript.",
+            best_of=5,
+            beam_size=5,
+            word_timestamps=True,
+        )
+
+        whisper_alignments = []
+        transcript_text = ""
+        for seg in segments:
+            transcript_text += seg.text
+            whisper_alignments += clean_whisper_alignments(seg.words)
+
+        # Transcript change required to match gentle's tokenization with
+        # Whisper's word alignments
+        transcript_text = transcript_text.replace("% ", " percent ")
+        return {
+            "language": info.language,
+            "language_probability": info.language_probability,
+            "duration": info.duration,
+            "transcript_text": transcript_text,
+            "whisper_alignments": whisper_alignments,
+        }
+
+    async def __call__(self, req: starlette.requests.Request):
+        request = await req.json()
+        return await self.transcribe(file_path=request["filepath"])
+
+
+entrypoint = WhisperModel.bind()