add: openai streaming response

whisper_fastapi.py

@@ -1,3 +1,6 @@
+import tqdm
+import json
+from fastapi.responses import StreamingResponse
 import wave
 import pydub
 import io
@@ -257,6 +260,9 @@ async def transcription(
     file: UploadFile = File(...),
     prompt: str = Form(""),
     response_format: str = Form("json"),
+    task: str = Form("transcribe"),
+    language: str = Form("und"),
+    vad_filter: bool = Form(False),
 ):
     """Transcription endpoint
 
@@ -266,10 +272,62 @@ async def transcription(
     # timestamp as filename, keep original extension
     options = get_options(initial_prompt=prompt)
 
+    # special function for streaming response (OpenAI API does not have this)
+    if response_format == "stream":
+
+        def gen():
+            segments, info = transcriber.model.transcribe(
+                audio=io.BytesIO(file.file.read()),
+                language=None if language == "und" else language,  # type: ignore
+                task=task,
+                beam_size=options.beam_size,
+                best_of=options.best_of,
+                patience=options.patience,
+                length_penalty=options.length_penalty,
+                repetition_penalty=options.repetition_penalty,
+                no_repeat_ngram_size=options.no_repeat_ngram_size,
+                temperature=options.temperature,
+                compression_ratio_threshold=options.compression_ratio_threshold,
+                log_prob_threshold=options.log_prob_threshold,
+                no_speech_threshold=options.no_speech_threshold,
+                condition_on_previous_text=options.condition_on_previous_text,
+                prompt_reset_on_temperature=options.prompt_reset_on_temperature,
+                initial_prompt=options.initial_prompt,
+                suppress_blank=options.suppress_blank,
+                suppress_tokens=options.suppress_tokens,
+                word_timestamps=True
+                if options.print_colors
+                else options.word_timestamps,
+                prepend_punctuations=options.prepend_punctuations,
+                append_punctuations=options.append_punctuations,
+                vad_filter=vad_filter,
+                vad_parameters=None,
+            )
+            print(
+                "Detected language '%s' with probability %f"
+                % (info.language, info.language_probability)
+            )
+            last_pos = 0
+            with tqdm.tqdm(total=info.duration, unit="seconds", disable=True) as pbar:
+                for segment in segments:
+                    start, end, text = segment.start, segment.end, segment.text
+                    pbar.update(end - last_pos)
+                    last_pos = end
+                    data = segment._asdict()
+                    data["total"] = info.duration
+                    data["text"] = ccc.convert(data["text"])
+                    yield "data: " + json.dumps(data, ensure_ascii=False) + "\n\n"
+            yield "data: [DONE]\n\n"
+
+        return StreamingResponse(
+            gen(),
+            media_type="text/event-stream",
+        )
+
     result: Any = transcriber.inference(
         audio=io.BytesIO(file.file.read()),
-        task="transcribe",
+        task=task,
-        language=None,  # type: ignore
+        language=None if language == "und" else language,  # type: ignore
         verbose=False,
         live=False,
         options=options,