Add Argmax Sortformer

.gitignore

@@ -45,4 +45,6 @@ inference_outputs/
 miscellaneous/
 
 # Default openbench-cli output directory
-downloaded_datasets/
+downloaded_datasets/
+
+*.sh

src/openbench/engine/whisperkitpro_engine.py

@@ -83,24 +83,28 @@ class WhisperKitProConfig(BaseModel):
         description="The compute units to use for the audio encoder. Default is CPU_AND_NE.",
     )
     text_decoder_compute_units: ct.ComputeUnit = Field(
-        ct.ComputeUnit.CPU_AND_GPU,
+        ct.ComputeUnit.CPU_AND_NE,
         description="The compute units to use for the text decoder. Default is CPU_AND_GPU.",
     )
     diarization: bool = Field(
         False,
         description="Whether to perform diarization",
     )
-    orchestration_strategy: Literal["word", "segment"] = Field(
-        "segment",
-        description="The orchestration strategy to use either `word` or `segment`",
+    diarization_mode: Literal["realtime", "prerecorded"] = Field(
+        "prerecorded",
+        description="Sortformer streaming mode: `realtime` (1.04s latency) or `prerecorded` (9.84s latency). This is only applicable when `engine` is `sortformer`.",
+    )
+    orchestration_strategy: Literal["segment", "subsegment"] = Field(
+        "subsegment",
+        description="The orchestration strategy to use either `segment` or `subsegment`",
     )
     speaker_models_path: str | None = Field(
         None,
         description="The path to the speaker models directory",
     )
-    clusterer_version: Literal["pyannote3", "pyannote4"] = Field(
-        "pyannote4",
-        description="The version of the clusterer to use",
+    engine: Literal["pyannote", "sortformer"] = Field(
+        "pyannote",
+        description="The engine to use. If `sortformer` the diarization model used is Sortformer, otherwise it is pyannote.",
     )
     use_exclusive_reconciliation: bool = Field(
         False,
@@ -158,6 +162,7 @@ def generate_cli_args(self, model_path: Path | None = None) -> list[str]:
                 COMPUTE_UNITS_MAPPER[self.text_decoder_compute_units],
                 "--fast-load",
                 str(self.fast_load).lower(),
+                "--verbose",
             ]
         )
 
@@ -171,9 +176,15 @@ def generate_cli_args(self, model_path: Path | None = None) -> list[str]:
         if self.diarization:
             args.extend(["--diarization"])
             args.extend(["--orchestration-strategy", self.orchestration_strategy])
+
             # Add rttm path
             args.extend(["--rttm-path", self.rttm_path])
-            args.extend(["--clusterer-version", self.clusterer_version])
+            args.extend(["--engine", self.engine])
+
+            # Only add diarization mode if using Sortformer
+            if self.engine == "sortformer":
+                args.extend(["--diarization-mode", self.diarization_mode])
+
             # If speaker models path is provided use it
             if self.speaker_models_path:
                 args.extend(["--speaker-models-path", self.speaker_models_path])

src/openbench/pipeline/diarization/speakerkit.py

@@ -23,48 +23,68 @@
 TEMP_AUDIO_DIR = Path("audio_temp")
 
 
-class SpeakerKitPipelineConfig(DiarizationPipelineConfig):
-    cli_path: str = Field(..., description="The absolute path to the SpeakerKit CLI")
-    clusterer_version: Literal["pyannote3", "pyannote4"] = Field(
-        "pyannote4", description="The version of the clusterer to use"
-    )
-    model_path: str | None = Field(None, description="The absolute path to the SpeakerKit model")
-
-
 class SpeakerKitInput(TypedDict):
     audio_path: Path
     output_path: Path
     num_speakers: int | None
 
 
-class SpeakerKitCli:
-    def __init__(self, config: SpeakerKitPipelineConfig):
-        self.cli_path = config.cli_path
-        self.model_path = config.model_path
-        self.clusterer_version = config.clusterer_version
+class SpeakerKitPipelineConfig(DiarizationPipelineConfig):
+    cli_path: str = Field(..., description="The absolute path to the SpeakerKit CLI")
+    model_path: str | None = Field(None, description="The absolute path to the SpeakerKit model directory")
+    engine: Literal["pyannote", "sortformer"] = Field("pyannote", description="The engine to use")
 
-    def __call__(self, speakerkit_input: SpeakerKitInput) -> tuple[Path, float]:
+    @property
+    def is_sortformer(self) -> bool:
+        return self.engine == "sortformer"
+
+    def generate_cli_args(self, inputs: SpeakerKitInput) -> list[str]:
         cmd = [
             self.cli_path,
             "diarize",
             "--audio-path",
-            str(speakerkit_input["audio_path"]),
+            str(inputs["audio_path"]),
             "--rttm-path",
-            str(speakerkit_input["output_path"]),
-            "--clusterer-version",
-            self.clusterer_version,
+            str(inputs["output_path"]),
+            "--engine",
+            self.engine,
             "--verbose",
         ]
 
-        if self.model_path:
+        if self.model_path is not None:
             cmd.extend(["--model-path", self.model_path])
 
-        if speakerkit_input["num_speakers"] is not None:
-            cmd.extend(["--num-speakers", str(speakerkit_input["num_speakers"])])
+        if inputs["num_speakers"] is not None:
+            cmd.extend(["--num-speakers", str(inputs["num_speakers"])])
 
         if "SPEAKERKIT_API_KEY" in os.environ:
             cmd.extend(["--api-key", os.environ["SPEAKERKIT_API_KEY"]])
 
+        return cmd
+
+    def parse_stdout(self, stdout: str) -> float:
+        # Default pattern for pyannote models
+        pattern = r"Model Load Time:\s+\d+\.\d+\s+ms\nTotal Time:\s+(\d+\.\d+)\s+ms"
+        divisor = 1000.0
+
+        # if model is sortfomer we override the pattern and divisor
+        if self.is_sortformer:
+            pattern = r"Prediction time:\s+(\d+\.\d+)\s+seconds"
+            divisor = 1.0
+
+        matches = re.search(pattern, stdout)
+        if matches is None:
+            raise ValueError(f"Could not parse prediction time from stdout: {stdout!r}")
+        return float(matches.group(1)) / divisor
+
+
+class SpeakerKitCli:
+    def __init__(self, config: SpeakerKitPipelineConfig):
+        self.config = config
+
+    def __call__(self, speakerkit_input: SpeakerKitInput) -> tuple[Path, float]:
+        cmd = self.config.generate_cli_args(speakerkit_input)
+
         try:
             result = subprocess.run(cmd, check=True, capture_output=True, text=True)
             logger.debug(f"Diarization CLI stdout:\n{result.stdout}")
@@ -81,11 +101,9 @@ def __call__(self, speakerkit_input: SpeakerKitInput) -> tuple[Path, float]:
         speakerkit_input["audio_path"].unlink()
 
         # Parse stdout and take the total time it took to diarize
-        pattern = r"Model Load Time:\s+\d+\.\d+\s+ms\nTotal Time:\s+(\d+\.\d+)\s+ms"
-        matches = re.search(pattern, result.stdout)
-        total_time = float(matches.group(1))
+        total_time = self.config.parse_stdout(result.stdout)
 
-        return speakerkit_input["output_path"], total_time / 1000
+        return speakerkit_input["output_path"], total_time
 
 
 @register_pipeline

src/openbench/pipeline/orchestration/orchestration_whisperkitpro.py

@@ -69,13 +69,17 @@ class WhisperKitProOrchestrationConfig(OrchestrationConfig):
         ComputeUnit.CPU_AND_NE,
         description="The compute units to use for the text decoder. Default is CPU_AND_NE.",
     )
-    orchestration_strategy: Literal["word", "segment"] = Field(
-        "segment",
-        description="The orchestration strategy to use either `word` or `segment`",
+    orchestration_strategy: Literal["segment", "subsegment"] = Field(
+        "subsegment",
+        description="The orchestration strategy to use either `segment` or `subsegment`",
     )
-    clusterer_version: Literal["pyannote3", "pyannote4"] = Field(
-        "pyannote4",
-        description="The version of the clusterer to use",
+    engine: Literal["pyannote", "sortformer"] = Field(
+        "pyannote",
+        description="The engine to use. If `sortformer` the diarization model used is Sortformer, otherwise it is pyannote.",
+    )
+    diarization_mode: Literal["realtime", "prerecorded"] = Field(
+        "prerecorded",
+        description="Sortformer streaming mode: `realtime` (1.04s latency) or `prerecorded` (9.84s latency). This is only applicable when `engine` is `sortformer`.",
     )
     use_exclusive_reconciliation: bool = Field(
         False,
@@ -107,7 +111,8 @@ def build_pipeline(self) -> WhisperKitPro:
             chunking_strategy="vad",
             diarization=True,
             orchestration_strategy=self.config.orchestration_strategy,
-            clusterer_version_string=self.config.clusterer_version,
+            engine=self.config.engine,
+            diarization_mode=self.config.diarization_mode,
             use_exclusive_reconciliation=self.config.use_exclusive_reconciliation,
             fast_load=self.config.fast_load,
         )

src/openbench/pipeline/pipeline_aliases.py

@@ -112,9 +112,23 @@ def register_pipeline_aliases() -> None:
         default_config={
             "out_dir": "./speakerkit-report",
             "cli_path": os.getenv("SPEAKERKIT_CLI_PATH"),
-            "clusterer_version": "pyannote4",
+            "engine": "pyannote",
         },
-        description="SpeakerKit speaker diarization pipeline. Requires CLI installation and API key. Set `SPEAKERKIT_CLI_PATH` and `SPEAKERKIT_API_KEY` env vars. For access to the CLI binary contact speakerkitpro@argmaxinc.com",
+        description="SpeakerKit speaker diarization pipeline using community-1 model from pyannote. Requires CLI installation and API key. Set `SPEAKERKIT_CLI_PATH` and `SPEAKERKIT_API_KEY` env vars. For access to the CLI binary contact speakerkitpro@argmaxinc.com",
+    )
+
+    PipelineRegistry.register_alias(
+        "speakerkit-sortformer-compressed",
+        SpeakerKitPipeline,
+        default_config={
+            "out_dir": "./speakerkit-sortformer-report",
+            "cli_path": os.getenv("SPEAKERKIT_CLI_PATH"),
+            "engine": "sortformer",
+        },
+        description=(
+            "SpeakerKit speaker diarization pipeline using Sortformer model compressed to 94MB. Requires CLI installation and API key. "
+            "Set `SPEAKERKIT_CLI_PATH` and `SPEAKERKIT_API_KEY` env vars. For access to the CLI binary contact speakerkitpro@argmaxinc.com."
+        ),
     )
 
     PipelineRegistry.register_alias(
@@ -203,8 +217,8 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/whisperkit-pro",
             "model_variant": "openai_whisper-tiny",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the tiny version of the model. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -217,8 +231,8 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/whisperkit-pro",
             "model_variant": "openai_whisper-large-v3",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the large-v3 version of the model. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -231,8 +245,8 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/whisperkit-pro",
             "model_variant": "openai_whisper-large-v3-v20240930",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the large-v3-v20240930 version of the model (which is the same as large-v3-turbo from OpenAI). Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -245,8 +259,8 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/whisperkit-pro",
             "model_variant": "openai_whisper-large-v3-v20240930_626MB",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the large-v3-v20240930 version of the model compressed to 626MB (which is the same as large-v3-turbo from OpenAI). Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -259,8 +273,8 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/parakeetkit-pro",
             "model_variant": "nvidia_parakeet-v2",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the parakeet-v2 version of the model. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -273,22 +287,39 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/parakeetkit-pro",
             "model_variant": "nvidia_parakeet-v2_476MB",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the parakeet-v2 version of the model compressed to 476MB. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
     )
 
+    PipelineRegistry.register_alias(
+        "whisperkitpro-orchestration-parakeet-v2-compressed-sortformer-compressed",
+        WhisperKitProOrchestrationPipeline,
+        default_config={
+            "repo_id": "argmaxinc/parakeetkit-pro",
+            "model_variant": "nvidia_parakeet-v2_476MB",
+            "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
+            "orchestration_strategy": "subsegment",
+            "engine": "sortformer",
+            "diarization_mode": "prerecorded",
+        },
+        description=(
+            "WhisperKitPro orchestration pipeline using the parakeet-v2 version of the model compressed to 476MB and using Sortformer for diarization. "
+            "Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var."
+        ),
+    )
+
     PipelineRegistry.register_alias(
         "whisperkitpro-orchestration-parakeet-v3",
         WhisperKitProOrchestrationPipeline,
         default_config={
             "repo_id": "argmaxinc/parakeetkit-pro",
             "model_variant": "nvidia_parakeet-v3",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the parakeet-v3 version of the model. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
@@ -301,13 +332,30 @@ def register_pipeline_aliases() -> None:
             "repo_id": "argmaxinc/parakeetkit-pro",
             "model_variant": "nvidia_parakeet-v3_494MB",
             "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
-            "orchestration_strategy": "segment",
-            "clusterer_version_string": "pyannote4",
+            "orchestration_strategy": "subsegment",
+            "engine": "pyannote",
             "use_exclusive_reconciliation": True,
         },
         description="WhisperKitPro orchestration pipeline using the parakeet-v3 version of the model compressed to 494MB. Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var.",
     )
 
+    PipelineRegistry.register_alias(
+        "whisperkitpro-orchestration-parakeet-v3-compressed-sortformer-compressed",
+        WhisperKitProOrchestrationPipeline,
+        default_config={
+            "repo_id": "argmaxinc/parakeetkit-pro",
+            "model_variant": "nvidia_parakeet-v3_494MB",
+            "cli_path": os.getenv("WHISPERKITPRO_CLI_PATH"),
+            "orchestration_strategy": "subsegment",
+            "engine": "sortformer",
+            "diarization_mode": "prerecorded",
+        },
+        description=(
+            "WhisperKitPro orchestration pipeline using the parakeet-v3 version of the model compressed to 494MB and using Sortformer for diarization. "
+            "Requires `WHISPERKITPRO_CLI_PATH` env var and depending on your permissions also `WHISPERKITPRO_API_KEY` env var."
+        ),
+    )
+
     PipelineRegistry.register_alias(
         "openai-orchestration",
         OpenAIOrchestrationPipeline,