diff --git a/.importlinter b/.importlinter
index 3959aa5d..aef764a2 100644
--- a/.importlinter
+++ b/.importlinter
@@ -16,7 +16,6 @@ source_modules =
     aai_cli.config
     aai_cli.config_builder
     aai_cli.context
-    aai_cli.der
     aai_cli.environments
     aai_cli.errors
     aai_cli.eval_data
@@ -76,7 +75,6 @@ source_modules =
     aai_cli.client
     aai_cli.config
     aai_cli.config_builder
-    aai_cli.der
     aai_cli.environments
     aai_cli.errors
     aai_cli.eval_data
diff --git a/Formula/assembly.rb b/Formula/assembly.rb
index d7d65928..7e1d21f1 100644
--- a/Formula/assembly.rb
+++ b/Formula/assembly.rb
@@ -13,17 +13,19 @@ class Assembly < Formula
     sha256 cellar: :any, arm64_sonoma: "daa913744f7176cf27100ea3860b2eba2c72438737eada3dfd7122860ac8d38b"
   end
 
-  depends_on "pkgconf" => :build      # cffi / cryptography native builds
-  depends_on "rust" => :build         # pydantic-core, jiter, cryptography
+  depends_on "pkgconf" => :build      # cffi native build (sounddevice; +cryptography on Linux)
+  depends_on "rust" => :build         # pydantic-core, jiter (+cryptography on Linux)
   depends_on "cloudflared"           # public quick-tunnel for `assembly share`
   depends_on "ffmpeg"                # decode non-WAV/URL audio (transcribe/stream)
-  depends_on "libyaml"               # pyyaml native extension
-  depends_on "openssl@3"             # cryptography linkage
   depends_on "portaudio"             # sounddevice (audio capture)
   depends_on "python@3.13"
 
-  # Linux-only keyring backend (D-Bus Secret Service); not built on macOS.
+  # Linux-only keyring backend (D-Bus Secret Service); not built on macOS, which
+  # uses the Keychain backend instead. secretstorage pulls in cryptography (a
+  # Rust+OpenSSL source build), so both it and openssl@3 stay off the macOS bottle.
   on_linux do
+    depends_on "openssl@3" # cryptography linkage
+
     resource "jeepney" do
       url "https://files.pythonhosted.org/packages/7b/6f/357efd7602486741aa73ffc0617fb310a29b588ed0fd69c2399acbb85b0c/jeepney-0.9.0.tar.gz"
       sha256 "cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732"
@@ -33,6 +35,11 @@ class Assembly < Formula
       url "https://files.pythonhosted.org/packages/1c/03/e834bcd866f2f8a49a85eaff47340affa3bfa391ee9912a952a1faa68c7b/secretstorage-3.5.0.tar.gz"
       sha256 "f04b8e4689cbce351744d5537bf6b1329c6fc68f91fa666f60a380edddcd11be"
     end
+
+    resource "cryptography" do
+      url "https://files.pythonhosted.org/packages/9f/a9/db8f313fdcd85d767d4973515e1db101f9c71f95fced83233de224673757/cryptography-48.0.0.tar.gz"
+      sha256 "5c3932f4436d1cccb036cb0eaef46e6e2db91035166f1ad6505c3c9d5a635920"
+    end
   end
 
   resource "annotated-doc" do
@@ -80,11 +87,6 @@ class Assembly < Formula
     sha256 "08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"
   end
 
-  resource "cryptography" do
-    url "https://files.pythonhosted.org/packages/9f/a9/db8f313fdcd85d767d4973515e1db101f9c71f95fced83233de224673757/cryptography-48.0.0.tar.gz"
-    sha256 "5c3932f4436d1cccb036cb0eaef46e6e2db91035166f1ad6505c3c9d5a635920"
-  end
-
   resource "distro" do
     url "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz"
     sha256 "2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"
@@ -145,11 +147,6 @@ class Assembly < Formula
     sha256 "ae9c051469102a61ef0927100baeeb4546f78d180c9b0948281d08eaf44c191e"
   end
 
-  resource "joblib" do
-    url "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz"
-    sha256 "8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3"
-  end
-
   resource "keyring" do
     url "https://files.pythonhosted.org/packages/43/4b/674af6ef2f97d56f0ab5153bf0bfa28ccb6c3ed4d1babf4305449668807b/keyring-25.7.0.tar.gz"
     sha256 "fe01bd85eb3f8fb3dd0405defdeac9a5b4f6f0439edbb3149577f244a2e8245b"
@@ -170,16 +167,6 @@ class Assembly < Formula
     sha256 "48e8f4d9e7e5878571ecf6f2b4e57634f93cd474cc8cfbd2376f2d11b396e30d"
   end
 
-  resource "narwhals" do
-    url "https://files.pythonhosted.org/packages/62/3c/c4ef2164a71c1a63d7f1ae411c4082c5fa872405106db60a4b7114989ad7/narwhals-2.22.1.tar.gz"
-    sha256 "d62920805a0a43b7ff8b54b0c0d3142d796f8a9301836ada37e573d6a33cbcd9"
-  end
-
-  resource "numpy" do
-    url "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz"
-    sha256 "f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda"
-  end
-
   resource "openai" do
     url "https://files.pythonhosted.org/packages/3c/a6/5815fe2e2aca74b36c650d1bd43b69827cee568073d0d2d9b6fc5aaac80c/openai-2.41.0.tar.gz"
     sha256 "db5c362acd6604b84f076abbefa66826ea4b46ecba2954ed866e6a149a1352c0"
@@ -190,11 +177,6 @@ class Assembly < Formula
     sha256 "ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661"
   end
 
-  resource "pandas" do
-    url "https://files.pythonhosted.org/packages/f8/87/4341c6252d1c47b08768c3d25ac487362bf403f0313ddae4a2a26c9b1b4c/pandas-3.0.3.tar.gz"
-    sha256 "696a4a00a2a2a35d4e5deb3fc946641b96c944f02230e4f76137fe35d806c4fc"
-  end
-
   resource "platformdirs" do
     url "https://files.pythonhosted.org/packages/d7/47/e4501f49c178ae1d9f4a75073fda4204f52647993f075a9db4d14930e0c5/platformdirs-4.10.0.tar.gz"
     sha256 "31e761a6a0ca04faf7353ea759bdba55652be214725111e5aac52dfa29d4bef7"
@@ -205,21 +187,6 @@ class Assembly < Formula
     sha256 "28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855"
   end
 
-  resource "pyannote-core" do
-    url "https://files.pythonhosted.org/packages/a3/be/4a35ea31c685aef801f7f35c193e7766ca1bb948ae497a625cbfaa8c31ba/pyannote_core-6.0.1.tar.gz"
-    sha256 "4b4ada3276f6df4e073fa79166636e3597d0dcb5a0fe26014a3477867cc033fb"
-  end
-
-  resource "pyannote-database" do
-    url "https://files.pythonhosted.org/packages/65/45/6210274c187cc457e854be8b56c6819fa14376f27e7e2b6021b2aa02449a/pyannote_database-6.1.1.tar.gz"
-    sha256 "bbe76da738257a9e64061123d9694ad7e949c4f171d91a9269606d873528cd10"
-  end
-
-  resource "pyannote-metrics" do
-    url "https://files.pythonhosted.org/packages/74/ba/7dbc2f790d5e321e46dc1e250ff00b69cdefc0c5695b811e96a4b932cddd/pyannote_metrics-4.1.tar.gz"
-    sha256 "afe24c54ee0799e8cfbe8ee85fa517793c3450bb7eae8fedd1a77ccec0343f7e"
-  end
-
   resource "pycparser" do
     url "https://files.pythonhosted.org/packages/1b/7d/92392ff7815c21062bea51aa7b87d45576f649f16458d78b7cf94b9ab2e6/pycparser-3.0.tar.gz"
     sha256 "600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29"
@@ -245,21 +212,11 @@ class Assembly < Formula
     sha256 "6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f"
   end
 
-  resource "python-dateutil" do
-    url "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz"
-    sha256 "37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"
-  end
-
   resource "python-dotenv" do
     url "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz"
     sha256 "2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3"
   end
 
-  resource "pyyaml" do
-    url "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz"
-    sha256 "d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"
-  end
-
   resource "questionary" do
     url "https://files.pythonhosted.org/packages/f6/45/eafb0bba0f9988f6a2520f9ca2df2c82ddfa8d67c95d6625452e97b204a5/questionary-2.1.1.tar.gz"
     sha256 "3d7e980292bb0107abaa79c68dd3eee3c561b83a0f89ae482860b181c8bd412d"
@@ -275,46 +232,21 @@ class Assembly < Formula
     sha256 "edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36"
   end
 
-  resource "scikit-learn" do
-    url "https://files.pythonhosted.org/packages/fa/6f/37092bdb25f712817231799fc5674d8e704066a8a70c1d2d40517e18b4ab/scikit_learn-1.9.0.tar.gz"
-    sha256 "8833266989d3a5110178a9fae30783675460724d0e1efb13b14901d2c660c557"
-  end
-
-  resource "scipy" do
-    url "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz"
-    sha256 "95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0"
-  end
-
   resource "shellingham" do
     url "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz"
     sha256 "8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"
   end
 
-  resource "six" do
-    url "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz"
-    sha256 "ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
-  end
-
   resource "sniffio" do
     url "https://files.pythonhosted.org/packages/a2/87/a6771e1546d97e7e041b6ae58d80074f81b7d5121207425c964ddf5cfdbd/sniffio-1.3.1.tar.gz"
     sha256 "f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"
   end
 
-  resource "sortedcontainers" do
-    url "https://files.pythonhosted.org/packages/e8/c4/ba2f8066cceb6f23394729afe52f3bf7adec04bf9ed2c820b39e19299111/sortedcontainers-2.4.0.tar.gz"
-    sha256 "25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"
-  end
-
   resource "sounddevice" do
     url "https://files.pythonhosted.org/packages/2a/f9/2592608737553638fca98e21e54bfec40bf577bb98a61b2770c912aab25e/sounddevice-0.5.5.tar.gz"
     sha256 "22487b65198cb5bf2208755105b524f78ad173e5ab6b445bdab1c989f6698df3"
   end
 
-  resource "threadpoolctl" do
-    url "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz"
-    sha256 "8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e"
-  end
-
   resource "tomli-w" do
     url "https://files.pythonhosted.org/packages/19/75/241269d1da26b624c0d5e110e8149093c759b7a286138f4efd61a60e75fe/tomli_w-1.2.0.tar.gz"
     sha256 "2dd14fac5a47c27be9cd4c976af5a12d87fb1f0b4512f81d69cce3b35ae25021"
@@ -340,11 +272,6 @@ class Assembly < Formula
     sha256 "ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"
   end
 
-  resource "tzdata" do
-    url "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz"
-    sha256 "9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10"
-  end
-
   resource "wcwidth" do
     url "https://files.pythonhosted.org/packages/2c/ee/afaf0f85a9a18fe47a67f1e4422ed6cf1fe642f0ae0a2f81166231303c52/wcwidth-0.7.0.tar.gz"
     sha256 "90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0"
@@ -369,8 +296,8 @@ def install
     # --help imports the full command tree, so a resource missing from this
     # formula fails here instead of on the user's first real command.
     assert_match "transcribe", shell_output("#{bin}/assembly --help")
-    # The `assembly eval` scoring stack (jiwer/pyannote) is imported lazily, so
+    # The `assembly eval` WER scoring stack (jiwer) is imported lazily, so
     # --help can't catch it being absent — probe it directly.
-    system libexec/"bin/python", "-c", "import jiwer, pyannote.metrics"
+    system libexec/"bin/python", "-c", "import jiwer"
   end
 end
diff --git a/README.md b/README.md
index 3b35cebb..ff7a53dc 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ neither.
 - **Real-time streaming**: `assembly stream` transcribes the microphone, a file, or a URL live — on macOS it can capture system audio too.
 - **Voice agent**: `assembly agent` runs a full-duplex spoken conversation in your terminal (use headphones).
 - **LLM Gateway**: `assembly llm` prompts an LLM over a transcript, stdin, or a live stream (`assembly stream --llm "summarize as I talk"`).
-- **Model evaluation**: `assembly eval` transcribes a Hugging Face dataset (with built-in aliases for common benchmarks: `assembly eval tedlium`) or a local `.csv`/`.jsonl` manifest and scores WER against its references (plus DER with `--speaker-labels`) — handy for picking a speech model.
+- **Model evaluation**: `assembly eval` transcribes a Hugging Face dataset (with built-in aliases for common benchmarks: `assembly eval tedlium`) or a local `.csv`/`.jsonl` manifest and scores WER against its references — handy for picking a speech model.
 - **Starter apps**: `assembly init` scaffolds a self-contained FastAPI + HTML app (`audio-transcription`, `live-captions`, `voice-agent`).
 - **Code generation**: add `--show-code` to `transcribe`/`stream`/`agent` to print the equivalent Python SDK script instead of running.
 - **Account self-service**: `assembly keys` / `balance` / `usage` / `limits` / `sessions` / `audit` via browser login.
diff --git a/aai_cli/commands/evaluate.py b/aai_cli/commands/evaluate.py
index 2c9e20a6..2a04b511 100644
--- a/aai_cli/commands/evaluate.py
+++ b/aai_cli/commands/evaluate.py
@@ -1,9 +1,8 @@
 """`assembly eval` — transcribe an evaluation dataset and score it against references.
 
-WER (via jiwer) against the dataset's reference texts; with ``--speaker-labels``
-also DER (via pyannote.metrics) against its reference speaker turns. The module
-is named ``evaluate`` because importing a module named ``eval`` would shadow the
-builtin; the command itself registers as ``eval``.
+WER (via jiwer) against the dataset's reference texts. The module is named
+``evaluate`` because importing a module named ``eval`` would shadow the builtin;
+the command itself registers as ``eval``.
 """
 
 from __future__ import annotations
@@ -16,9 +15,9 @@
 import typer
 from rich.console import RenderableType
 
-from aai_cli import client, der, eval_data, help_panels, jsonshape, options, output, wer
+from aai_cli import client, eval_data, help_panels, jsonshape, options, output, wer
 from aai_cli.context import AppState, run_command
-from aai_cli.errors import CLIError, NotAuthenticated, UsageError
+from aai_cli.errors import CLIError, NotAuthenticated
 from aai_cli.help_text import examples_epilog
 
 app = typer.Typer()
@@ -36,45 +35,28 @@ def _pct(value: object) -> str:
     return f"{jsonshape.as_float(value):.2%}"
 
 
-def _hypothesis_turns(transcript: aai.Transcript) -> list[der.Turn]:
-    """The transcript's diarized utterances as DER hypothesis turns (ms → seconds)."""
-    return [
-        der.Turn(
-            speaker=str(getattr(utterance, "speaker", "")),
-            start=jsonshape.as_float(getattr(utterance, "start", None)) / 1000,
-            end=jsonshape.as_float(getattr(utterance, "end", None)) / 1000,
-        )
-        for utterance in jsonshape.object_list(getattr(transcript, "utterances", None))
-    ]
-
-
 @dataclass(frozen=True)
 class _ItemResult:
-    """One scored row: the emitted dict plus the scores kept for pooling."""
+    """One scored row: the emitted dict plus the score kept for pooling."""
 
     row: dict[str, object]
     words: wer.Score | None
-    speakers: der.DerScore | None
 
 
 def _failed_result(item: eval_data.EvalItem, err: CLIError) -> _ItemResult:
     """A row whose transcription failed: the error rides along, no scores pooled."""
-    return _ItemResult(row={"item": item.item_id, "error": err.message}, words=None, speakers=None)
+    return _ItemResult(row={"item": item.item_id, "error": err.message}, words=None)
 
 
-def _score_item(
-    item: eval_data.EvalItem, transcript: aai.Transcript, *, collar: float
-) -> _ItemResult:
-    row: dict[str, object] = {"item": item.item_id}
-    words: wer.Score | None = None
-    speakers: der.DerScore | None = None
-    if item.reference is not None:
-        words = wer.score(item.reference, str(transcript.text or ""))
-        row.update({"words": words.words, "errors": words.errors, "wer": words.wer})
-    if item.turns is not None:
-        speakers = der.score(item.turns, _hypothesis_turns(transcript), collar=collar)
-        row["der"] = speakers.der
-    return _ItemResult(row=row, words=words, speakers=speakers)
+def _score_item(item: eval_data.EvalItem, transcript: aai.Transcript) -> _ItemResult:
+    words = wer.score(item.reference, str(transcript.text or ""))
+    row: dict[str, object] = {
+        "item": item.item_id,
+        "words": words.words,
+        "errors": words.errors,
+        "wer": words.wer,
+    }
+    return _ItemResult(row=row, words=words)
 
 
 def _pooled_metrics(results: list[_ItemResult]) -> dict[str, object]:
@@ -84,15 +66,6 @@ def _pooled_metrics(results: list[_ItemResult]) -> dict[str, object]:
     if word_scores:
         total = wer.pooled(word_scores)
         metrics.update({"words": total.words, "errors": total.errors, "wer": total.wer})
-    der_scores = [result.speakers for result in results if result.speakers is not None]
-    if der_scores:
-        pooled = der.pooled(der_scores)
-        metrics["der"] = pooled.der
-        metrics["der_breakdown"] = {
-            "missed": pooled.missed / pooled.total,
-            "false_alarm": pooled.false_alarm / pooled.total,
-            "confusion": pooled.confusion / pooled.total,
-        }
     return metrics
 
 
@@ -193,13 +166,6 @@ def _summary(payload: dict[str, object]) -> str:
         parts.append(
             f"WER {_pct(payload.get('wer'))} ({errors} {noun} / {payload.get('words')} words)"
         )
-    if "der" in payload:
-        breakdown = jsonshape.as_mapping(payload.get("der_breakdown")) or {}
-        parts.append(
-            f"DER {_pct(payload.get('der'))} (missed {_pct(breakdown.get('missed'))} · "
-            f"false alarm {_pct(breakdown.get('false_alarm'))} · "
-            f"confusion {_pct(breakdown.get('confusion'))})"
-        )
     return output.heading("   ".join(parts))
 
 
@@ -214,12 +180,10 @@ def _pct_cell(row: dict[str, object], key: str) -> str:
 
 def _render(payload: dict[str, object]) -> RenderableType:
     has_wer = "wer" in payload
-    has_der = "der" in payload
     has_failed = "failed" in payload
     columns = [
         "ITEM",
         *(["WORDS", "ERRORS", "WER"] if has_wer else []),
-        *(["DER"] if has_der else []),
         *(["ERROR"] if has_failed else []),
     ]
     table = output.data_table(*columns)
@@ -227,8 +191,6 @@ def _render(payload: dict[str, object]) -> RenderableType:
         cells = [str(row.get("item"))]
         if has_wer:
             cells += [_cell(row, "words"), _cell(row, "errors"), _pct_cell(row, "wer")]
-        if has_der:
-            cells.append(_pct_cell(row, "der"))
         if has_failed:
             cells.append(_cell(row, "error"))
         table.add_row(*cells)
@@ -251,10 +213,6 @@ def _render(payload: dict[str, object]) -> RenderableType:
                 "Compare models on your own audio",
                 "assembly eval calls.csv --speech-model universal-3-pro",
             ),
-            (
-                "Score diarization too (WER + DER)",
-                "assembly eval agent-calls.jsonl --speaker-labels",
-            ),
             (
                 "More rows, transcribed four at a time",
                 "assembly eval librispeech --limit 50 --concurrency 4",
@@ -263,10 +221,6 @@ def _render(payload: dict[str, object]) -> RenderableType:
                 "Evaluate non-English audio",
                 "assembly eval commonvoice --subset fr --language-code fr",
             ),
-            (
-                "DER on a diarization benchmark",
-                "assembly eval callhome --speaker-labels",
-            ),
         ]
     ),
 )
@@ -295,18 +249,6 @@ def evaluate(
     language_code: str | None = typer.Option(
         None, "--language-code", help="Force a language (e.g. en_us)."
     ),
-    speaker_labels: bool = typer.Option(
-        False,
-        "--speaker-labels",
-        help="Diarize and also score DER against the dataset's reference speaker turns (speakers/timestamps_start/timestamps_end columns, in seconds).",
-    ),
-    collar: float | None = typer.Option(
-        None,
-        "--collar",
-        min=0.0,
-        help="DER forgiveness (seconds) around each reference turn boundary "
-        "(default: 1.0; needs --speaker-labels).",
-    ),
     concurrency: int = typer.Option(
         1,
         "--concurrency",
@@ -321,8 +263,7 @@ def evaluate(
     text; both are normalized first (lowercased, punctuation stripped) so style
     differences don't count as errors, and the summary pools total errors over
     total reference words. Handy for picking a model: run once per
-    --speech-model and compare. --speaker-labels also diarizes and scores DER
-    against reference speaker turns.
+    --speech-model and compare.
 
     Datasets come from the Hugging Face Hub (any public dataset its viewer
     serves with audio + reference columns; gated ones need HF_TOKEN), a local
@@ -333,16 +274,10 @@ def evaluate(
     (meetings), gigaspeech, peoples (real-world US English), commonvoice
     (English; --subset fr etc. for its 98 other locales), voxpopuli
     (parliament speech), switchboard (phone calls), expresso (expressive
-    speech), loquacious, and callhome (phone calls with speaker turns, for
-    --speaker-labels).
+    speech), loquacious, and callhome (phone calls).
     """
 
     def body(state: AppState, json_mode: bool) -> None:
-        if collar is not None and not speaker_labels:
-            raise UsageError(
-                "--collar only applies when diarization is being scored.",
-                suggestion="Add --speaker-labels.",
-            )
         # Resolve credentials before any dataset download: a signed-out user must
         # not pull the whole dataset only to fail at the first transcription.
         api_key = state.resolve_api_key()
@@ -353,12 +288,10 @@ def body(state: AppState, json_mode: bool) -> None:
             audio_column=audio_column,
             text_column=text_column,
             limit=limit,
-            with_speakers=speaker_labels,
         )
         transcription_config = aai.TranscriptionConfig(
             speech_models=[speech_model.value] if speech_model else None,
             language_code=language_code,
-            speaker_labels=speaker_labels or None,
         )
         outcomes = _transcripts(
             api_key,
@@ -371,7 +304,7 @@ def body(state: AppState, json_mode: bool) -> None:
         results = [
             _failed_result(item, outcome)
             if isinstance(outcome, CLIError)
-            else _score_item(item, outcome, collar=collar if collar is not None else 1.0)
+            else _score_item(item, outcome)
             for item, outcome in zip(
                 data.items,
                 outcomes,
diff --git a/aai_cli/der.py b/aai_cli/der.py
deleted file mode 100644
index e9de35a9..00000000
--- a/aai_cli/der.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Diarization error rate (DER) scoring for `assembly eval --speaker-labels`.
-
-A thin shim over :mod:`pyannote.metrics` — the de-facto standard DER
-implementation, including its optimal reference↔hypothesis speaker mapping —
-so the alignment math is never re-derived here. pyannote drags numpy/scipy/
-pandas along, so it is imported lazily inside :func:`score`; the dataclasses
-here stay import-cheap for the command layer. No SDK, no Rich.
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from pydantic import TypeAdapter
-
-if TYPE_CHECKING:
-    from pyannote.core import Annotation
-
-# pyannote types the metric call as a bare float; with ``detailed=True`` it
-# actually returns the components dict — validate that shape instead of casting.
-_COMPONENTS: TypeAdapter[dict[str, float]] = TypeAdapter(dict[str, float])
-
-
-@dataclass(frozen=True)
-class Turn:
-    """One speaker turn: who spoke from ``start`` to ``end`` (seconds)."""
-
-    speaker: str
-    start: float
-    end: float
-
-
-@dataclass(frozen=True)
-class DerScore:
-    """DER components in seconds of speech; pooled across files for corpus DER."""
-
-    missed: float
-    false_alarm: float
-    confusion: float
-    total: float
-
-    @property
-    def der(self) -> float:
-        return (self.missed + self.false_alarm + self.confusion) / self.total
-
-
-def _annotation(turns: list[Turn]) -> Annotation:
-    from pyannote.core import Annotation, Segment
-
-    annotation = Annotation()
-    # The list index keys each track, so two overlapping turns by the same
-    # speaker don't collapse into one.
-    for track, turn in enumerate(turns):
-        annotation[Segment(turn.start, turn.end), track] = turn.speaker
-    return annotation
-
-
-def score(reference: list[Turn], hypothesis: list[Turn], *, collar: float = 0.0) -> DerScore:
-    """Score hypothesis speaker turns against the reference diarization.
-
-    The caller guarantees a non-empty reference (the dataset loader rejects
-    rows without speaker turns), so ``DerScore.der`` is always well-defined.
-    ``collar`` forgives that many seconds around each reference turn boundary.
-    """
-    from pyannote.core import Segment, Timeline
-    from pyannote.metrics.diarization import DiarizationErrorRate
-
-    metric = DiarizationErrorRate(collar=collar)
-    # An explicit evaluation extent (audio start through the last turn either
-    # side heard); without it pyannote warns about approximating the UEM.
-    extent = max(turn.end for turn in [*reference, *hypothesis])
-    components: object = metric(
-        _annotation(reference),
-        _annotation(hypothesis),
-        uem=Timeline([Segment(0.0, extent)]),
-        detailed=True,
-    )
-    detail = _COMPONENTS.validate_python(components)
-    return DerScore(
-        missed=detail["missed detection"],
-        false_alarm=detail["false alarm"],
-        confusion=detail["confusion"],
-        total=detail["total"],
-    )
-
-
-def pooled(scores: list[DerScore]) -> DerScore:
-    """Corpus-level score: error seconds over total reference speech seconds."""
-    return DerScore(
-        missed=sum(item.missed for item in scores),
-        false_alarm=sum(item.false_alarm for item in scores),
-        confusion=sum(item.confusion for item in scores),
-        total=sum(item.total for item in scores),
-    )
diff --git a/aai_cli/eval_data.py b/aai_cli/eval_data.py
index 128e79f2..6bb9808b 100644
--- a/aai_cli/eval_data.py
+++ b/aai_cli/eval_data.py
@@ -11,11 +11,6 @@
   API ingests directly. Gated/private datasets authenticate via ``HF_TOKEN``.
   The common benchmarks also have short **aliases** (``ALIASES``) that fill in
   the hub id plus the subset/split/audio-column defaults each set needs.
-
-With ``with_speakers`` (the ``--speaker-labels`` flag), rows must also carry
-diarization references as the parallel ``speakers`` / ``timestamps_start`` /
-``timestamps_end`` arrays the Hugging Face diarization datasets use (seconds);
-reference text then becomes optional, since diarization sets often have none.
 """
 
 from __future__ import annotations
@@ -26,7 +21,7 @@
 from dataclasses import dataclass
 from pathlib import Path
 
-from aai_cli import der, eval_hf_api, jsonshape, wer
+from aai_cli import eval_hf_api, jsonshape, wer
 from aai_cli.errors import APIError, CLIError, UsageError
 
 _MANIFEST_SUFFIXES = (".csv", ".jsonl")
@@ -38,9 +33,6 @@
 # and manifest tools use (HF audio datasets, Common Voice, NeMo manifests).
 _AUDIO_COLUMNS = ("audio", "audio_filepath", "audio_url", "path", "file")
 _TEXT_COLUMNS = ("text", "sentence", "transcription", "transcript", "normalized_text")
-# Diarization references: the parallel-array convention the Hugging Face
-# diarization datasets (diarizers-community/*) use, in seconds.
-_SPEAKER_COLUMNS = ("speakers", "timestamps_start", "timestamps_end")
 
 
 @dataclass(frozen=True)
@@ -78,13 +70,11 @@ class Alias:
 
 @dataclass(frozen=True)
 class EvalItem:
-    """One evaluation row: an audio source (path or URL) plus its references —
-    text for WER, speaker turns for DER (each optional, never both absent)."""
+    """One evaluation row: an audio source (path or URL) plus its reference text."""
 
     item_id: str
     audio: str
-    reference: str | None
-    turns: list[der.Turn] | None = None
+    reference: str
 
 
 @dataclass(frozen=True)
@@ -104,7 +94,6 @@ def load(
     audio_column: str | None = None,
     text_column: str | None = None,
     limit: int,
-    with_speakers: bool = False,
 ) -> EvalDataset:
     """Load evaluation items from a local manifest or a Hugging Face dataset id."""
     path = Path(dataset)
@@ -118,7 +107,6 @@ def load(
             audio_column=audio_column,
             text_column=text_column,
             limit=limit,
-            with_speakers=with_speakers,
         )
     alias = ALIASES.get(dataset)
     if alias is not None:
@@ -133,7 +121,6 @@ def load(
         audio_column=audio_column,
         text_column=text_column,
         limit=limit,
-        with_speakers=with_speakers,
     )
 
 
@@ -164,22 +151,9 @@ def _resolve_columns(
     *,
     audio_column: str | None,
     text_column: str | None,
-    with_speakers: bool,
-) -> tuple[str, str | None]:
-    """The (audio, text) columns to read. Text is only optional when speaker
-    turns are being scored instead — diarization datasets often carry no text."""
+) -> tuple[str, str]:
+    """The (audio, text) columns to read."""
     audio_col = _pick_column(columns, audio_column, _AUDIO_COLUMNS, "--audio-column")
-    if with_speakers:
-        missing = [name for name in _SPEAKER_COLUMNS if name not in columns]
-        if missing:
-            raise UsageError(
-                f"--speaker-labels needs speaker-turn columns; missing: {', '.join(missing)} "
-                f"(columns: {', '.join(columns)}).",
-                suggestion="Rows carry parallel speakers/timestamps_start/timestamps_end "
-                "arrays in seconds (arrays need a .jsonl manifest, not .csv).",
-            )
-        if text_column is None and not any(name in columns for name in _TEXT_COLUMNS):
-            return audio_col, None
     return audio_col, _pick_column(columns, text_column, _TEXT_COLUMNS, "--text-column")
 
 
@@ -193,33 +167,10 @@ def _checked_reference(item_id: str, reference: str) -> str:
     return reference
 
 
-def _row_reference(cells: dict[str, object], text_col: str | None, item_id: str) -> str | None:
-    if text_col is None:
-        return None
+def _row_reference(cells: dict[str, object], text_col: str, item_id: str) -> str:
     return _checked_reference(item_id, str(cells.get(text_col) or ""))
 
 
-def _row_turns(cells: dict[str, object], item_id: str) -> list[der.Turn]:
-    """The row's reference speaker turns from the parallel-array columns."""
-    speakers = jsonshape.object_list(cells.get("speakers"))
-    starts = jsonshape.object_list(cells.get("timestamps_start"))
-    ends = jsonshape.object_list(cells.get("timestamps_end"))
-    if not speakers or len(starts) != len(speakers) or len(ends) != len(speakers):
-        raise UsageError(
-            f"{item_id} needs non-empty, equal-length speakers/timestamps_start/"
-            "timestamps_end arrays.",
-            suggestion="Each row lists who spoke plus matching start/end seconds.",
-        )
-    return [
-        der.Turn(
-            speaker=str(speakers[i]),
-            start=jsonshape.as_float(starts[i]),
-            end=jsonshape.as_float(ends[i]),
-        )
-        for i in range(len(speakers))
-    ]
-
-
 # ---------------------------------------------------------------- local manifests
 
 
@@ -248,7 +199,6 @@ def _load_manifest(
     audio_column: str | None,
     text_column: str | None,
     limit: int,
-    with_speakers: bool,
 ) -> EvalDataset:
     if not path.is_file():
         raise CLIError(
@@ -271,12 +221,9 @@ def _load_manifest(
         list(rows[0]),
         audio_column=audio_column,
         text_column=text_column,
-        with_speakers=with_speakers,
     )
     items = [
-        _manifest_item(
-            path, index, row, audio_col=audio_col, text_col=text_col, with_speakers=with_speakers
-        )
+        _manifest_item(path, index, row, audio_col=audio_col, text_col=text_col)
         for index, row in enumerate(rows[:limit], start=1)
     ]
     return EvalDataset(label=path.name, items=items)
@@ -288,8 +235,7 @@ def _manifest_item(
     row: dict[str, object],
     *,
     audio_col: str,
-    text_col: str | None,
-    with_speakers: bool,
+    text_col: str,
 ) -> EvalItem:
     audio = str(row.get(audio_col) or "")
     if not audio:
@@ -311,7 +257,6 @@ def _manifest_item(
         item_id=item_id,
         audio=audio,
         reference=_row_reference(row, text_col, item_id),
-        turns=_row_turns(row, item_id) if with_speakers else None,
     )
 
 
@@ -338,8 +283,7 @@ def _hf_item(
     split_name: str,
     *,
     audio_col: str,
-    text_col: str | None,
-    with_speakers: bool,
+    text_col: str,
 ) -> EvalItem:
     cells = jsonshape.as_mapping(row.get("row")) or {}
     item_id = f"{split_name}[{row.get('row_idx')}]"
@@ -347,7 +291,6 @@ def _hf_item(
         item_id=item_id,
         audio=_audio_source(cells.get(audio_col), column=audio_col, item_id=item_id),
         reference=_row_reference(cells, text_col, item_id),
-        turns=_row_turns(cells, item_id) if with_speakers else None,
     )
 
 
@@ -359,7 +302,6 @@ def _load_hf(
     audio_column: str | None,
     text_column: str | None,
     limit: int,
-    with_speakers: bool,
 ) -> EvalDataset:
     if not _HF_ID_RE.match(dataset):
         raise UsageError(
@@ -381,14 +323,8 @@ def _load_hf(
         list(jsonshape.as_mapping(rows[0].get("row")) or {}),
         audio_column=audio_column,
         text_column=text_column,
-        with_speakers=with_speakers,
     )
     return EvalDataset(
         label=f"{dataset} · {config}/{split_name}",
-        items=[
-            _hf_item(
-                row, split_name, audio_col=audio_col, text_col=text_col, with_speakers=with_speakers
-            )
-            for row in rows
-        ],
+        items=[_hf_item(row, split_name, audio_col=audio_col, text_col=text_col) for row in rows],
     )
diff --git a/aai_cli/wer.py b/aai_cli/wer.py
index b1d59233..df281ece 100644
--- a/aai_cli/wer.py
+++ b/aai_cli/wer.py
@@ -4,10 +4,10 @@
 the alignment math is never re-derived here. Texts are normalized the way ASR
 benchmarks conventionally are (lowercase, punctuation stripped, whitespace
 collapsed) so a transcript isn't penalized for casing or punctuation style.
-jiwer is imported lazily inside the scoring functions (mirroring ``der.py``'s
-lazy pyannote import) so this module stays import-cheap for the command layer —
-an install that's missing the eval scoring stack must still run every other
-command. No SDK, no Rich: the command layer owns all rendering.
+jiwer is imported lazily inside the scoring functions so this module stays
+import-cheap for the command layer — an install that's missing the eval scoring
+stack must still run every other command. No SDK, no Rich: the command layer
+owns all rendering.
 """
 
 from __future__ import annotations
diff --git a/pyproject.toml b/pyproject.toml
index b19ea4bd..bdf40452 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,11 +49,9 @@ dependencies = [
     "packaging>=24.0",
     # audioop (used for PCM resampling) left the stdlib in 3.13; this backport provides it.
     "audioop-lts>=0.2; python_version >= '3.13'",
-    # `assembly eval` scoring: jiwer for WER, pyannote.metrics for DER — the de-facto
-    # standard implementations of each. pyannote drags numpy/scipy/pandas, so der.py
-    # imports it lazily to keep CLI startup fast.
+    # `assembly eval` WER scoring uses jiwer (the de-facto standard WER
+    # implementation); wer.py imports it lazily to keep CLI startup fast.
     "jiwer>=4.0",
-    "pyannote-metrics>=4.0",
 ]
 
 [project.urls]
diff --git a/tests/__snapshots__/test_cli_output_snapshots.ambr b/tests/__snapshots__/test_cli_output_snapshots.ambr
index fcc007a1..18b8ede0 100644
--- a/tests/__snapshots__/test_cli_output_snapshots.ambr
+++ b/tests/__snapshots__/test_cli_output_snapshots.ambr
@@ -247,8 +247,7 @@
    text; both are normalized first (lowercased, punctuation stripped) so style
    differences don't count as errors, and the summary pools total errors over
    total reference words. Handy for picking a model: run once per
-   --speech-model and compare. --speaker-labels also diarizes and scores DER
-   against reference speaker turns.
+   --speech-model and compare.
   
    Datasets come from the Hugging Face Hub (any public dataset its viewer
    serves with audio + reference columns; gated ones need HF_TOKEN), a local
@@ -259,8 +258,7 @@
    (meetings), gigaspeech, peoples (real-world US English), commonvoice
    (English; --subset fr etc. for its 98 other locales), voxpopuli
    (parliament speech), switchboard (phone calls), expresso (expressive
-   speech), loquacious, and callhome (phone calls with speaker turns, for
-   --speaker-labels).
+   speech), loquacious, and callhome (phone calls).
   
   ╭─ Arguments ──────────────────────────────────────────────────────────────────╮
   │ *    dataset      TEXT  Hugging Face dataset id, or a local .csv/.jsonl      │
@@ -268,37 +266,27 @@
   │                         [required]                                           │
   ╰──────────────────────────────────────────────────────────────────────────────╯
   ╭─ Options ────────────────────────────────────────────────────────────────────╮
-  │ --split                   TEXT                     Hugging Face split to     │
+  │ --split                  TEXT                      Hugging Face split to     │
   │                                                    score (default: test).    │
-  │ --subset                  TEXT                     Hugging Face              │
+  │ --subset                 TEXT                      Hugging Face              │
   │                                                    config/subset name (e.g.  │
   │                                                    a language).              │
-  │ --limit                   INTEGER RANGE            Rows to evaluate (1-100). │
-  │                           [1<=x<=100]              [default: 10]             │
-  │ --audio-column            TEXT                     Audio column name         │
+  │ --limit                  INTEGER RANGE             Rows to evaluate (1-100). │
+  │                          [1<=x<=100]               [default: 10]             │
+  │ --audio-column           TEXT                      Audio column name         │
   │                                                    (default: auto-detect).   │
-  │ --text-column             TEXT                     Reference text column     │
+  │ --text-column            TEXT                      Reference text column     │
   │                                                    name (default:            │
   │                                                    auto-detect).             │
-  │ --speech-model            [universal-3-pro|univer  Speech model to evaluate. │
-  │                           sal-2]                                             │
-  │ --language-code           TEXT                     Force a language (e.g.    │
+  │ --speech-model           [universal-3-pro|univers  Speech model to evaluate. │
+  │                          al-2]                                               │
+  │ --language-code          TEXT                      Force a language (e.g.    │
   │                                                    en_us).                   │
-  │ --speaker-labels                                   Diarize and also score    │
-  │                                                    DER against the dataset's │
-  │                                                    reference speaker turns   │
-  │                                                    (speakers/timestamps_sta… │
-  │                                                    columns, in seconds).     │
-  │ --collar                  FLOAT RANGE [x>=0.0]     DER forgiveness (seconds) │
-  │                                                    around each reference     │
-  │                                                    turn boundary (default:   │
-  │                                                    1.0; needs                │
-  │                                                    --speaker-labels).        │
-  │ --concurrency             INTEGER RANGE [x>=1]     How many items to         │
+  │ --concurrency            INTEGER RANGE [x>=1]      How many items to         │
   │                                                    transcribe at once        │
   │                                                    (sequential by default).  │
   │                                                    [default: 1]              │
-  │ --json            -j                               Output the rows and       │
+  │ --json           -j                                Output the rows and       │
   │                                                    summary as one JSON       │
   │                                                    object.                   │
   │ --help                                             Show this message and     │
@@ -310,14 +298,10 @@
    $ assembly eval tedlium
    Compare models on your own audio
    $ assembly eval calls.csv --speech-model universal-3-pro
-   Score diarization too (WER + DER)
-   $ assembly eval agent-calls.jsonl --speaker-labels
    More rows, transcribed four at a time
    $ assembly eval librispeech --limit 50 --concurrency 4
    Evaluate non-English audio
    $ assembly eval commonvoice --subset fr --language-code fr
-   DER on a diarization benchmark
-   $ assembly eval callhome --speaker-labels
   
   
   
diff --git a/tests/test_der.py b/tests/test_der.py
deleted file mode 100644
index cfcffd69..00000000
--- a/tests/test_der.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""DER scoring (`aai_cli.der`): pyannote-backed alignment, collar, pooling."""
-
-import dataclasses
-
-import pytest
-
-from aai_cli import der
-
-
-def _assign(obj, attribute, value):
-    setattr(obj, attribute, value)
-
-
-def test_turns_and_scores_are_immutable_values():
-    with pytest.raises(dataclasses.FrozenInstanceError):
-        _assign(der.Turn(speaker="a", start=0.0, end=1.0), "speaker", "b")
-    score = der.DerScore(missed=0.0, false_alarm=0.0, confusion=0.0, total=1.0)
-    with pytest.raises(dataclasses.FrozenInstanceError):
-        _assign(score, "total", 2.0)
-
-
-REF = [
-    der.Turn(speaker="alice", start=0.0, end=10.0),
-    der.Turn(speaker="bob", start=10.0, end=20.0),
-]
-HYP = [der.Turn(speaker="A", start=0.0, end=12.0), der.Turn(speaker="B", start=12.0, end=20.0)]
-
-
-def test_optimal_speaker_mapping_scores_only_the_overlap_error():
-    # alice↔A and bob↔B map optimally; the 10-12s stretch is confusion.
-    score = der.score(REF, HYP)
-    assert score == der.DerScore(missed=0.0, false_alarm=0.0, confusion=2.0, total=20.0)
-    assert score.der == 0.1
-
-
-def test_collar_forgives_boundary_slop():
-    # A 4s collar swallows the 10-12s confusion around the 10s boundary.
-    score = der.score(REF, HYP, collar=4.0)
-    assert score == der.DerScore(missed=0.0, false_alarm=0.0, confusion=0.0, total=12.0)
-    assert score.der == 0.0
-
-
-def test_empty_hypothesis_is_all_missed_detection():
-    score = der.score(REF, [])
-    assert score == der.DerScore(missed=20.0, false_alarm=0.0, confusion=0.0, total=20.0)
-    assert score.der == 1.0
-
-
-def test_hypothesis_speech_beyond_the_reference_is_false_alarm():
-    # The evaluation extent covers the hypothesis too — speech past the
-    # reference's end counts as false alarm rather than being cropped away.
-    score = der.score(
-        [der.Turn(speaker="a", start=0.0, end=10.0)],
-        [der.Turn(speaker="A", start=0.0, end=15.0)],
-    )
-    assert score == der.DerScore(missed=0.0, false_alarm=5.0, confusion=0.0, total=10.0)
-    assert score.der == 0.5
-
-
-def test_overlapping_turns_by_one_speaker_keep_their_own_tracks():
-    turns = [der.Turn(speaker="a", start=0.0, end=10.0), der.Turn(speaker="a", start=5.0, end=15.0)]
-    assert der.score(turns, turns).der == 0.0
-
-
-def test_pooled_sums_components():
-    total = der.pooled(
-        [
-            der.DerScore(missed=1.0, false_alarm=2.0, confusion=3.0, total=10.0),
-            der.DerScore(missed=0.0, false_alarm=0.0, confusion=0.0, total=10.0),
-        ]
-    )
-    assert total == der.DerScore(missed=1.0, false_alarm=2.0, confusion=3.0, total=20.0)
-    assert total.der == 0.3
diff --git a/tests/test_eval_command.py b/tests/test_eval_command.py
index 93310fd5..5a384b04 100644
--- a/tests/test_eval_command.py
+++ b/tests/test_eval_command.py
@@ -1,4 +1,4 @@
-"""`assembly eval` behavior: WER/DER scoring, rendering, flags, error paths.
+"""`assembly eval` behavior: WER scoring, rendering, flags, error paths.
 
 The transcription boundary (`client.transcribe`) is mocked; datasets are real
 temp manifests so the command exercises the loader end to end.
@@ -28,8 +28,8 @@ def _auth():
     config.set_api_key("default", "sk_live")
 
 
-def _transcript(text, utterances=None):
-    return SimpleNamespace(text=text, utterances=utterances)
+def _transcript(text):
+    return SimpleNamespace(text=text)
 
 
 def _write_wer_manifest(tmp_path):
@@ -40,29 +40,6 @@ def _write_wer_manifest(tmp_path):
     )
 
 
-def _write_speaker_manifest(tmp_path, *, with_text=False):
-    (tmp_path / "a.wav").write_bytes(b"fake-audio")
-    row = {
-        "audio": "a.wav",
-        "speakers": ["alice", "bob"],
-        "timestamps_start": [0.0, 10.0],
-        "timestamps_end": [10.0, 20.0],
-    }
-    if with_text:
-        row["text"] = "hello there"
-    (tmp_path / "manifest.jsonl").write_text(json.dumps(row), encoding="utf-8")
-
-
-def _diarized_utterances():
-    # Against the manifest's alice 0-10s / bob 10-20s reference: 2s of confusion
-    # around the 10s boundary. The default 1s collar forgives 10-10.5s and trims
-    # 2s of boundary speech from the 20s total -> DER 1.5/18 (see test_der.py).
-    return [
-        SimpleNamespace(speaker="A", start=0, end=12000),
-        SimpleNamespace(speaker="B", start=12000, end=20000),
-    ]
-
-
 def _mock_transcribe(mocker, results):
     return mocker.patch(
         "aai_cli.commands.evaluate.client.transcribe",
@@ -92,7 +69,6 @@ def test_wer_table_with_per_file_and_pooled_scores(tmp_path, mocker):
     assert "1 errors" not in result.output
     assert "manifest.csv" in result.output
     assert "default model" in result.output
-    assert "DER" not in result.output
 
 
 def test_summary_pluralizes_errors(tmp_path, mocker):
@@ -117,7 +93,6 @@ def test_json_payload_shape(tmp_path, mocker):
     assert payload["words"] == 4
     assert payload["errors"] == 1
     assert payload["wer"] == 0.25
-    assert "der" not in payload
     assert payload["rows"][0] == {"item": "a.wav", "words": 2, "errors": 0, "wer": 0.0}
     assert payload["rows"][1] == {"item": "b.wav", "words": 2, "errors": 1, "wer": 0.5}
     assert "failed" not in payload  # only present when a row failed
@@ -131,9 +106,7 @@ def test_speech_model_flag_reaches_config_and_output(tmp_path, mocker, model):
     result = runner.invoke(app, ["eval", "manifest.csv", "--speech-model", model, "--json"])
     assert result.exit_code == 0
     assert _payload_of(result)["speech_model"] == model
-    tx_config = tx.call_args.kwargs["config"]
-    assert tx_config.speech_models == [model]
-    assert tx_config.speaker_labels is None  # not requested -> omitted, not False
+    assert tx.call_args.kwargs["config"].speech_models == [model]
 
 
 def test_no_speech_model_leaves_speech_models_unset(tmp_path, mocker):
@@ -163,88 +136,6 @@ def test_speech_model_named_in_human_header(tmp_path, mocker):
     assert "default model" not in result.output
 
 
-def test_speaker_labels_scores_der_only_when_dataset_has_no_text(tmp_path, mocker):
-    _auth()
-    _write_speaker_manifest(tmp_path)
-    tx = _mock_transcribe(mocker, [_transcript("ignored", _diarized_utterances())])
-    result = runner.invoke(app, ["eval", "manifest.jsonl", "--speaker-labels"])
-    assert result.exit_code == 0
-    assert tx.call_args.kwargs["config"].speaker_labels is True
-    assert "DER" in result.output
-    assert "8.33%" in result.output  # 1.5/18 under the default 1s collar
-    # The summary names each DER component; here the error is pure confusion.
-    assert "missed 0.00%" in result.output
-    assert "false alarm 0.00%" in result.output
-    assert "confusion 8.33%" in result.output
-    assert "WER" not in result.output
-    assert "WORDS" not in result.output
-
-
-def test_speaker_labels_with_text_scores_both_metrics(tmp_path, mocker):
-    _auth()
-    _write_speaker_manifest(tmp_path, with_text=True)
-    _mock_transcribe(mocker, [_transcript("hello there", _diarized_utterances())])
-    result = runner.invoke(app, ["eval", "manifest.jsonl", "--speaker-labels", "--json"])
-    assert result.exit_code == 0
-    payload = _payload_of(result)
-    assert payload["wer"] == 0.0
-    assert payload["der"] == 1.5 / 18  # default 1s collar
-    assert payload["der_breakdown"] == {"missed": 0.0, "false_alarm": 0.0, "confusion": 1.5 / 18}
-    assert payload["rows"][0]["wer"] == 0.0
-    assert payload["rows"][0]["der"] == 1.5 / 18
-
-
-def test_both_metrics_render_in_one_table(tmp_path, mocker):
-    _auth()
-    _write_speaker_manifest(tmp_path, with_text=True)
-    _mock_transcribe(mocker, [_transcript("hello there", _diarized_utterances())])
-    result = runner.invoke(app, ["eval", "manifest.jsonl", "--speaker-labels"])
-    assert "WER" in result.output
-    assert "DER" in result.output
-    assert "8.33%" in result.output
-
-
-def test_der_counts_leading_speech_the_model_missed(tmp_path, mocker):
-    _auth()
-    (tmp_path / "a.wav").write_bytes(b"fake-audio")
-    row = {
-        "audio": "a.wav",
-        "speakers": ["alice"],
-        "timestamps_start": [0.0],
-        "timestamps_end": [10.0],
-    }
-    (tmp_path / "m.jsonl").write_text(json.dumps(row), encoding="utf-8")
-    # The hypothesis starts 1000ms in (1.0s exactly after ms→s conversion):
-    # 1s missed over 10s of reference speech, scored without collar forgiveness.
-    utterances = [SimpleNamespace(speaker="A", start=1000, end=10000)]
-    _mock_transcribe(mocker, [_transcript("x", utterances)])
-    result = runner.invoke(app, ["eval", "m.jsonl", "--speaker-labels", "--collar", "0", "--json"])
-    payload = _payload_of(result)
-    assert payload["der"] == 0.1
-    # All missed detection — distinguishes the breakdown keys from one another.
-    assert payload["der_breakdown"] == {"missed": 0.1, "false_alarm": 0.0, "confusion": 0.0}
-
-
-def test_der_counts_trailing_hypothesis_speech_as_false_alarm(tmp_path, mocker):
-    _auth()
-    (tmp_path / "a.wav").write_bytes(b"fake-audio")
-    row = {
-        "audio": "a.wav",
-        "speakers": ["alice"],
-        "timestamps_start": [0.0],
-        "timestamps_end": [10.0],
-    }
-    (tmp_path / "m.jsonl").write_text(json.dumps(row), encoding="utf-8")
-    # The hypothesis keeps "speaking" 5s past the 10s reference: 5s of false
-    # alarm over 10s of reference speech, scored without collar forgiveness.
-    utterances = [SimpleNamespace(speaker="A", start=0, end=15000)]
-    _mock_transcribe(mocker, [_transcript("x", utterances)])
-    result = runner.invoke(app, ["eval", "m.jsonl", "--speaker-labels", "--collar", "0", "--json"])
-    payload = _payload_of(result)
-    assert payload["der"] == 0.5
-    assert payload["der_breakdown"] == {"missed": 0.0, "false_alarm": 0.5, "confusion": 0.0}
-
-
 def _assign(obj, attribute, value):
     setattr(obj, attribute, value)
 
@@ -252,21 +143,11 @@ def _assign(obj, attribute, value):
 def test_item_results_are_immutable():
     from aai_cli.commands.evaluate import _ItemResult
 
-    result = _ItemResult(row={}, words=None, speakers=None)
+    result = _ItemResult(row={}, words=None)
     with pytest.raises(dataclasses.FrozenInstanceError):
         _assign(result, "words", None)
 
 
-def test_collar_flag_loosens_der(tmp_path, mocker):
-    _auth()
-    _write_speaker_manifest(tmp_path)
-    _mock_transcribe(mocker, [_transcript("ignored", _diarized_utterances())])
-    result = runner.invoke(
-        app, ["eval", "manifest.jsonl", "--speaker-labels", "--collar", "4.0", "--json"]
-    )
-    assert _payload_of(result)["der"] == 0.0
-
-
 def test_missing_transcript_text_scores_as_all_deletions(tmp_path, mocker):
     _auth()
     (tmp_path / "a.wav").write_bytes(b"fake-audio")
@@ -291,7 +172,6 @@ def test_loader_defaults(tmp_path, mocker):
     assert result.exit_code == 0
     kwargs = load.call_args.kwargs
     assert kwargs["limit"] == 10
-    assert kwargs["with_speakers"] is False
     assert kwargs["split"] is None and kwargs["subset"] is None
     assert kwargs["audio_column"] is None and kwargs["text_column"] is None
 
@@ -348,16 +228,6 @@ def fake_status(message, *, json_mode, quiet):
     assert seen == ["[1/2] Transcribing a.wav…", "[2/2] Transcribing b.wav…"]
 
 
-def test_collar_without_speaker_labels_is_a_usage_error(mocker):
-    # Mirrors transcribe's --speakers-expected guard: a silently inert flag is a bug.
-    tx = mocker.patch("aai_cli.commands.evaluate.client.transcribe", autospec=True)
-    result = runner.invoke(app, ["eval", "org/ds", "--collar", "0.5"])
-    assert result.exit_code == 2
-    assert "--collar only applies" in result.output
-    assert "Add --speaker-labels." in result.output
-    tx.assert_not_called()
-
-
 def test_missing_manifest_is_a_usage_failure(tmp_path):
     _auth()
     result = runner.invoke(app, ["eval", "nope.csv"])
diff --git a/tests/test_eval_data_hf.py b/tests/test_eval_data_hf.py
index ea38a59c..72c42311 100644
--- a/tests/test_eval_data_hf.py
+++ b/tests/test_eval_data_hf.py
@@ -10,7 +10,7 @@
 import httpx2 as httpx
 import pytest
 
-from aai_cli import der, eval_data, eval_hf_api
+from aai_cli import eval_data, eval_hf_api
 from aai_cli.errors import APIError, UsageError
 
 # ------------------------------------------------------- Hugging Face datasets
@@ -96,13 +96,6 @@ def test_hf_unusable_audio_cell_errors(monkeypatch):
     assert "test[0]" in exc.value.message and "no audio URL" in exc.value.message
 
 
-def test_hf_speaker_rows(monkeypatch):
-    row = _hf_row(speakers=["alice"], timestamps_start=[0.5], timestamps_end=[2.0])
-    _hf_handler(monkeypatch, splits=_ONE_SPLIT, rows=[row])
-    data = eval_data.load("org/ds", limit=1, with_speakers=True)
-    assert data.items[0].turns == [der.Turn(speaker="alice", start=0.5, end=2.0)]
-
-
 def test_hf_single_named_config_auto_picked(monkeypatch):
     seen = []
     splits = [{"config": "clean", "split": "test"}]
diff --git a/tests/test_eval_data_manifest.py b/tests/test_eval_data_manifest.py
index 9709e57d..aa8c5109 100644
--- a/tests/test_eval_data_manifest.py
+++ b/tests/test_eval_data_manifest.py
@@ -9,7 +9,7 @@
 
 import pytest
 
-from aai_cli import der, eval_data
+from aai_cli import eval_data
 from aai_cli.errors import CLIError, UsageError
 
 # ---------------------------------------------------------------- local manifests
@@ -48,7 +48,6 @@ def test_csv_manifest_loads_items(tmp_path):
     assert [item.item_id for item in data.items] == ["a.wav", "b.wav"]
     assert data.items[0].audio == str(tmp_path / "a.wav")
     assert data.items[0].reference == "hello there"
-    assert data.items[0].turns is None
 
 
 def test_manifest_respects_limit(tmp_path):
@@ -198,84 +197,3 @@ def test_split_and_subset_rejected_for_manifests(tmp_path):
         eval_data.load(str(manifest), split="test", limit=10)
     with pytest.raises(UsageError, match="local manifests"):
         eval_data.load(str(manifest), subset="default", limit=10)
-
-
-# ------------------------------------------------- manifests with speaker turns
-
-
-def _speaker_row(audio, **extra):
-    return {
-        "audio": audio,
-        "speakers": ["alice", "bob"],
-        "timestamps_start": [0.0, 10.0],
-        "timestamps_end": [10.0, 20.0],
-        **extra,
-    }
-
-
-def test_speaker_manifest_loads_turns_without_text(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.jsonl"
-    _write_jsonl(manifest, [_speaker_row("a.wav")])
-    data = eval_data.load(str(manifest), limit=10, with_speakers=True)
-    assert data.items[0].reference is None
-    assert data.items[0].turns == [
-        der.Turn(speaker="alice", start=0.0, end=10.0),
-        der.Turn(speaker="bob", start=10.0, end=20.0),
-    ]
-
-
-def test_speaker_manifest_with_text_scores_both(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.jsonl"
-    _write_jsonl(manifest, [_speaker_row("a.wav", text="hello world")])
-    data = eval_data.load(str(manifest), limit=10, with_speakers=True)
-    assert data.items[0].reference == "hello world"
-    assert data.items[0].turns is not None
-
-
-def test_speaker_manifest_with_explicit_text_column(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.jsonl"
-    _write_jsonl(manifest, [_speaker_row("a.wav", ref="hello world")])
-    data = eval_data.load(str(manifest), text_column="ref", limit=10, with_speakers=True)
-    assert data.items[0].reference == "hello world"
-
-
-def test_speakers_requested_but_columns_missing(tmp_path):
-    manifest = tmp_path / "m.jsonl"
-    _write_jsonl(manifest, [{"audio": "a.wav", "text": "x", "speakers": ["a"]}])
-    with pytest.raises(UsageError) as exc:
-        eval_data.load(str(manifest), limit=10, with_speakers=True)
-    assert "missing: timestamps_start, timestamps_end" in exc.value.message
-    assert exc.value.suggestion is not None and ".jsonl" in exc.value.suggestion
-
-
-def test_speaker_arrays_of_unequal_length_rejected(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.jsonl"
-    row = _speaker_row("a.wav")
-    row["timestamps_end"] = [10.0]
-    _write_jsonl(manifest, [row])
-    with pytest.raises(UsageError, match="equal-length"):
-        eval_data.load(str(manifest), limit=10, with_speakers=True)
-
-
-def test_empty_speaker_arrays_rejected(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.jsonl"
-    row = {"audio": "a.wav", "speakers": [], "timestamps_start": [], "timestamps_end": []}
-    _write_jsonl(manifest, [row])
-    with pytest.raises(UsageError, match="non-empty"):
-        eval_data.load(str(manifest), limit=10, with_speakers=True)
-
-
-def test_csv_cells_cannot_hold_speaker_arrays(tmp_path):
-    _write_audio(tmp_path, "a.wav")
-    manifest = tmp_path / "m.csv"
-    manifest.write_text(
-        'audio,speakers,timestamps_start,timestamps_end\na.wav,"[""alice""]","[0.0]","[10.0]"\n',
-        encoding="utf-8",
-    )
-    with pytest.raises(UsageError, match=r"equal-length|non-empty"):
-        eval_data.load(str(manifest), limit=10, with_speakers=True)
diff --git a/tests/test_import_hygiene.py b/tests/test_import_hygiene.py
index b9ce603d..4129e348 100644
--- a/tests/test_import_hygiene.py
+++ b/tests/test_import_hygiene.py
@@ -1,10 +1,10 @@
 """The CLI's import chain must stay free of the heavy eval scoring stack.
 
-`assembly eval`'s WER/DER scoring pulls in jiwer and pyannote.metrics (which
-drags numpy/scipy/pandas). Those imports are lazy by design (`wer.py`/`der.py`),
-so every other command keeps working on an install that doesn't ship the
-scoring stack — a module-scope `import jiwer` once crashed *every* invocation
-(even `--help`) of a Homebrew install whose formula lacked those resources.
+`assembly eval`'s WER scoring pulls in jiwer (and its rapidfuzz backend). That
+import is lazy by design (`wer.py`), so every other command keeps working on an
+install that doesn't ship the scoring stack — a module-scope `import jiwer` once
+crashed *every* invocation (even `--help`) of a Homebrew install whose formula
+lacked those resources.
 """
 
 from __future__ import annotations
@@ -15,7 +15,7 @@
 _PROBE = """
 import sys
 import aai_cli.main
-heavy = {"jiwer", "rapidfuzz", "pyannote", "numpy", "scipy", "pandas", "sklearn"}
+heavy = {"jiwer", "rapidfuzz"}
 loaded = sorted(heavy & {name.split(".")[0] for name in sys.modules})
 assert not loaded, f"CLI import eagerly loaded: {loaded}"
 """
diff --git a/uv.lock b/uv.lock
index ee36f30c..4874e92e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -29,7 +29,6 @@ dependencies = [
     { name = "openai" },
     { name = "packaging" },
     { name = "platformdirs" },
-    { name = "pyannote-metrics" },
     { name = "pydantic" },
     { name = "questionary" },
     { name = "rich" },
@@ -81,7 +80,6 @@ requires-dist = [
     { name = "openai", specifier = ">=2.41.0" },
     { name = "packaging", specifier = ">=24.0" },
     { name = "platformdirs", specifier = ">=4.10.0" },
-    { name = "pyannote-metrics", specifier = ">=4.0" },
     { name = "pydantic", specifier = ">=2.13.4" },
     { name = "questionary", specifier = ">=2.0.1" },
     { name = "rich", specifier = ">=15.0.0" },
@@ -1011,15 +1009,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/69/c9/172c525330c739a068c01050759a6f855ce16212db10a0359e690a03ac48/jiwer-4.0.0-py3-none-any.whl", hash = "sha256:7efaf0bd336b095d99ddef9dd67e1ee829d75d58aa2a81d9639870b01d6d95ea", size = 23034, upload-time = "2025-06-19T16:05:21.821Z" },
 ]
 
-[[package]]
-name = "joblib"
-version = "1.5.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" },
-]
-
 [[package]]
 name = "keyring"
 version = "25.7.0"
@@ -1255,15 +1244,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963, upload-time = "2025-04-22T14:54:22.983Z" },
 ]
 
-[[package]]
-name = "narwhals"
-version = "2.22.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/9c/1c/c80cb7719721a44846c6301ef118434bae30a423924bfad3a47f16bdc064/narwhals-2.22.0.tar.gz", hash = "sha256:6486282bb7e4b4ab55963efbd8be1451b764cc4874b74d1fd625eba9dc60b86f", size = 417565, upload-time = "2026-06-01T13:34:36.249Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/1f/b6/e7cdde7b8e90d5dff25b622f95833ef26567ad184c977278b93a1cbd5717/narwhals-2.22.0-py3-none-any.whl", hash = "sha256:1421797ede01789cc1537619dbc3f36f840737240f748fdb24a60a0225fc80be", size = 453815, upload-time = "2026-06-01T13:34:34.127Z" },
-]
-
 [[package]]
 name = "nodeenv"
 version = "1.10.0"
@@ -1273,67 +1253,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" },
 ]
 
-[[package]]
-name = "numpy"
-version = "2.4.6"
-source = { registry = "https://pypi.org/simple" }
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