[Frontend] add chunking audio for > 30s audio (#19597)

Signed-off-by: nguyenhoangthuan99 <thuanhppro12@gmail.com>

tests/entrypoints/openai/test_transcription_validation.py

@@ -74,19 +74,29 @@ async def test_bad_requests(mary_had_lamb):
                                                      language="hh",
                                                      temperature=0.0)
 
-        # Expect audio too long: repeat the timeseries
-        mary_had_lamb.seek(0)
-        audio, sr = librosa.load(mary_had_lamb)
-        repeated_audio = np.tile(audio, 10)
-        # Repeated audio to buffer
-        buffer = io.BytesIO()
-        sf.write(buffer, repeated_audio, sr, format='WAV')
-        buffer.seek(0)
-        with pytest.raises(openai.BadRequestError):
-            await client.audio.transcriptions.create(model=model_name,
-                                                     file=buffer,
-                                                     language="en",
-                                                     temperature=0.0)
+
+@pytest.mark.asyncio
+async def test_long_audio_request(mary_had_lamb):
+    model_name = "openai/whisper-large-v3-turbo"
+    server_args = ["--enforce-eager"]
+
+    mary_had_lamb.seek(0)
+    audio, sr = librosa.load(mary_had_lamb)
+    repeated_audio = np.tile(audio, 10)
+    # Repeated audio to buffer
+    buffer = io.BytesIO()
+    sf.write(buffer, repeated_audio, sr, format='WAV')
+    buffer.seek(0)
+    with RemoteOpenAIServer(model_name, server_args) as remote_server:
+        client = remote_server.get_async_client()
+        transcription = await client.audio.transcriptions.create(
+            model=model_name,
+            file=buffer,
+            language="en",
+            response_format="text",
+            temperature=0.0)
+        out = json.loads(transcription)['text']
+        assert out.count("Mary had a little lamb") == 10
 
 
 @pytest.mark.asyncio

vllm/entrypoints/openai/serving_transcription.py

@@ -2,11 +2,13 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
 import io
+import math
 import time
 from collections.abc import AsyncGenerator
 from math import ceil
 from typing import Final, Optional, Union, cast
 
+import numpy as np
 from fastapi import Request
 
 from vllm.config import ModelConfig
@@ -143,6 +145,8 @@ ISO639_1_OTHER_LANGS = {
 # As per https://platform.openai.com/docs/guides/speech-to-text#overview.
 # TODO configurable
 MAX_AUDIO_CLIP_FILESIZE_MB = 25
+OVERLAP_CHUNK_SECOND = 1
+MIN_ENERGY_WINDOW_SIZE = 1600  # 1600 ~ 100ms for 16000 Hz audio
 
 
 class OpenAIServingTranscription(OpenAIServing):
@@ -178,7 +182,7 @@ class OpenAIServingTranscription(OpenAIServing):
         self,
         request: TranscriptionRequest,
         audio_data: bytes,
-    ) -> tuple[PromptType, float]:
+    ) -> tuple[list[PromptType], float]:
         # Validate request
         # TODO language should be optional and can be guessed.
         # For now we default to en. See
@@ -206,22 +210,22 @@ class OpenAIServingTranscription(OpenAIServing):
             y, sr = librosa.load(bytes_)
 
         duration = librosa.get_duration(y=y, sr=sr)
-        if duration > self.max_audio_clip_s:
-            raise ValueError(
-                f"Maximum clip duration ({self.max_audio_clip_s}s) "
-                "exceeded.")
-
-        prompt = {
-            "encoder_prompt": {
-                "prompt": "",
-                "multi_modal_data": {
-                    "audio": (y, sr),
+        chunks = [y] if duration < 30 else self._split_audio(y, sr)
+        prompts = []
+        for i, chunk in enumerate(chunks):
+            prompt = {
+                "encoder_prompt": {
+                    "prompt": "",
+                    "multi_modal_data": {
+                        "audio": (chunk, sr),
+                    },
                 },
-            },
-            "decoder_prompt":
-            f"<|startoftranscript|>{lang_token}<|transcribe|><|notimestamps|>{request.prompt}"
-        }
-        return cast(PromptType, prompt), duration
+                "decoder_prompt":
+                f"<|startoftranscript|>{lang_token}<|transcribe|><|notimestamps|>{request.prompt}"
+                if i == 0 else ""
+            }
+            prompts.append(cast(PromptType, prompt))
+        return prompts, duration
 
     # TODO (varun) : Make verbose response work !
     async def create_transcription(
@@ -268,7 +272,7 @@ class OpenAIServingTranscription(OpenAIServing):
                     "Currently do not support PromptAdapter for Transcription."
                 )
 
-            prompt, duration_s = await self._preprocess_transcription(
+            prompts, duration_s = await self._preprocess_transcription(
                 request=request,
                 audio_data=audio_data,
             )
@@ -277,7 +281,8 @@ class OpenAIServingTranscription(OpenAIServing):
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))
 
-        result_generator: Optional[AsyncGenerator[RequestOutput, None]] = None
+        list_result_generator: Optional[list[AsyncGenerator[RequestOutput,
+                                                            None]]] = None
         try:
             # Unlike most decoder-only models, whisper generation length is not
             # constrained by the size of the input audio, which is mapped to a
@@ -288,32 +293,36 @@ class OpenAIServingTranscription(OpenAIServing):
 
             self._log_inputs(
                 request_id,
-                prompt['decoder_prompt'],  # type: ignore
+                prompts[0]['decoder_prompt'],  # type: ignore
                 params=sampling_params,
                 lora_request=None,
                 prompt_adapter_request=None)
 
-            result_generator = self.engine_client.generate(
-                prompt,
-                sampling_params,
-                request_id,
-            )
+            list_result_generator = [
+                self.engine_client.generate(
+                    prompt,
+                    sampling_params,
+                    request_id,
+                ) for prompt in prompts
+            ]
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))
 
         if request.stream:
             return self.transcription_stream_generator(request,
-                                                       result_generator,
+                                                       list_result_generator,
                                                        request_id,
                                                        request_metadata,
                                                        duration_s)
         # Non-streaming response.
         try:
-            assert result_generator is not None
-            async for op in result_generator:
-                result = op
-            return TranscriptionResponse(text=result.outputs[0].text)
+            assert list_result_generator is not None
+            text = ""
+            for result_generator in list_result_generator:
+                async for op in result_generator:
+                    text += op.outputs[0].text
+            return TranscriptionResponse(text=text)
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
         except ValueError as e:
@@ -322,7 +331,7 @@ class OpenAIServingTranscription(OpenAIServing):
 
     async def transcription_stream_generator(
             self, request: TranscriptionRequest,
-            result_generator: AsyncGenerator[RequestOutput, None],
+            list_result_generator: list[AsyncGenerator[RequestOutput, None]],
             request_id: str, request_metadata: RequestResponseMetadata,
             audio_duration_s: float) -> AsyncGenerator[str, None]:
         created_time = int(time.time())
@@ -335,60 +344,65 @@ class OpenAIServingTranscription(OpenAIServing):
         include_usage = request.stream_include_usage \
             if request.stream_include_usage else False
         include_continuous_usage = request.stream_continuous_usage_stats\
-              if include_usage and request.stream_continuous_usage_stats\
-                else False
+            if include_usage and request.stream_continuous_usage_stats\
+            else False
 
         try:
-            async for res in result_generator:
-                # On first result.
-                if res.prompt_token_ids is not None:
-                    # Do not account the 4-tokens `<|startoftranscript|>..`
-                    # Could be negative when language token is not specified.
-                    num_prompt_tokens = max(len(res.prompt_token_ids) - 4, 0)
-                    # NOTE(NickLucche) user can't pass encoder prompts directly
-                    # at least not to Whisper. One indicator of the encoder
-                    # amount of processing is the log-mel spectogram length.
-                    num_prompt_tokens += ceil(audio_duration_s *
-                                              self.model_sr / self.hop_length)
-
-                # We need to do it here, because if there are exceptions in
-                # the result_generator, it needs to be sent as the FIRST
-                # response (by the try...catch).
-
-                # Just one output (n=1) supported.
-                assert len(res.outputs) == 1
-                output = res.outputs[0]
-
-                delta_message = DeltaMessage(content=output.text)
-                completion_tokens += len(output.token_ids)
-
-                if output.finish_reason is None:
-                    # Still generating, send delta update.
-                    choice_data = TranscriptionResponseStreamChoice(
-                        delta=delta_message)
-                else:
-                    # Model is finished generating.
-                    choice_data = TranscriptionResponseStreamChoice(
-                        delta=delta_message,
-                        finish_reason=output.finish_reason,
-                        stop_reason=output.stop_reason)
-
-                chunk = TranscriptionStreamResponse(id=request_id,
-                                                    object=chunk_object_type,
-                                                    created=created_time,
-                                                    choices=[choice_data],
-                                                    model=model_name)
-
-                # handle usage stats if requested & if continuous
-                if include_continuous_usage:
-                    chunk.usage = UsageInfo(
-                        prompt_tokens=num_prompt_tokens,
-                        completion_tokens=completion_tokens,
-                        total_tokens=num_prompt_tokens + completion_tokens,
-                    )
-
-                data = chunk.model_dump_json(exclude_unset=True)
-                yield f"data: {data}\n\n"
+            for result_generator in list_result_generator:
+                async for res in result_generator:
+                    # On first result.
+                    if res.prompt_token_ids is not None:
+                        # Do not account the 4-tokens `<|startoftranscript|>..`
+                        # Could be negative when language token
+                        # is not specified.
+                        num_prompt_tokens = max(
+                            len(res.prompt_token_ids) - 4, 0)
+                        # NOTE(NickLucche) user can't pass encoder
+                        # prompts directly at least not to Whisper.
+                        # One indicator of the encoder amount of processing
+                        # is the log-mel spectogram length.
+                        num_prompt_tokens += ceil(
+                            audio_duration_s * self.model_sr / self.hop_length)
+
+                    # We need to do it here, because if there are exceptions in
+                    # the result_generator, it needs to be sent as the FIRST
+                    # response (by the try...catch).
+
+                    # Just one output (n=1) supported.
+                    assert len(res.outputs) == 1
+                    output = res.outputs[0]
+
+                    delta_message = DeltaMessage(content=output.text)
+                    completion_tokens += len(output.token_ids)
+
+                    if output.finish_reason is None:
+                        # Still generating, send delta update.
+                        choice_data = TranscriptionResponseStreamChoice(
+                            delta=delta_message)
+                    else:
+                        # Model is finished generating.
+                        choice_data = TranscriptionResponseStreamChoice(
+                            delta=delta_message,
+                            finish_reason=output.finish_reason,
+                            stop_reason=output.stop_reason)
+
+                    chunk = TranscriptionStreamResponse(
+                        id=request_id,
+                        object=chunk_object_type,
+                        created=created_time,
+                        choices=[choice_data],
+                        model=model_name)
+
+                    # handle usage stats if requested & if continuous
+                    if include_continuous_usage:
+                        chunk.usage = UsageInfo(
+                            prompt_tokens=num_prompt_tokens,
+                            completion_tokens=completion_tokens,
+                            total_tokens=num_prompt_tokens + completion_tokens,
+                        )
+
+                    data = chunk.model_dump_json(exclude_unset=True)
+                    yield f"data: {data}\n\n"
 
             # Once the final token is handled, if stream_options.include_usage
             # is sent, send the usage.
@@ -422,3 +436,52 @@ class OpenAIServingTranscription(OpenAIServing):
             yield f"data: {data}\n\n"
         # Send the final done message after all response.n are finished
         yield "data: [DONE]\n\n"
+
+    def _split_audio(self, audio_data: np.ndarray,
+                     sample_rate: int) -> list[np.ndarray]:
+        chunk_size = sample_rate * self.max_audio_clip_s
+        overlap_size = sample_rate * OVERLAP_CHUNK_SECOND
+        chunks = []
+        i = 0
+        while i < audio_data.shape[-1]:
+            if i + chunk_size >= audio_data.shape[-1]:
+                # handle last chunk
+                chunks.append(audio_data[..., i:])
+                break
+
+            # Find the best split point in the overlap region
+            search_start = i + chunk_size - overlap_size
+            search_end = min(i + chunk_size, audio_data.shape[-1])
+            split_point = self._find_split_point(audio_data, search_start,
+                                                 search_end)
+
+            # Extract chunk up to the split point
+            chunks.append(audio_data[..., i:split_point])
+            i = split_point
+        return chunks
+
+    def _find_split_point(self, wav: np.ndarray, start_idx: int,
+                          end_idx: int) -> int:
+        """Find the best point to split audio by 
+        looking for silence or low amplitude.
+        Args:
+            wav: Audio tensor [1, T]
+            start_idx: Start index of search region
+            end_idx: End index of search region
+        Returns:
+            Index of best splitting point
+        """
+        segment = wav[start_idx:end_idx]
+
+        # Calculate RMS energy in small windows
+        min_energy = math.inf
+        quietest_idx = 0
+        for i in range(0,
+                       len(segment) - MIN_ENERGY_WINDOW_SIZE,
+                       MIN_ENERGY_WINDOW_SIZE):
+            window = segment[i:i + MIN_ENERGY_WINDOW_SIZE]
+            energy = (window**2).mean()**0.5
+            if energy < min_energy:
+                quietest_idx = i + start_idx
+                min_energy = energy
+        return quietest_idx