Address review comments

.gitignore

@@ -192,5 +192,3 @@ cython_debug/
 #  refer to https://docs.cursor.com/context/ignore-files
 .cursorignore
 .cursorindexingignore
-bria.mp3
-sample_fr_hibiki_crepes.mp3

README.md

@@ -48,12 +48,6 @@ Here is how to choose which one to use:
   MLX is Apple's ML framework that allows you to use hardware acceleration on Apple silicon.
   If you want to run the model on a Mac or an iPhone, choose the MLX implementation.
 
-You can retrieve the sample files used in the following snippets via:
-```bash
-wget https://github.com/metavoiceio/metavoice-src/raw/main/assets/bria.mp3
-wget https://github.com/kyutai-labs/moshi/raw/refs/heads/main/data/sample_fr_hibiki_crepes.mp3
-```
-
 ### PyTorch implementation
 <a href="https://huggingface.co/kyutai/stt-2.6b-en" target="_blank" style="margin: 2px;">
     <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue" style="display: inline-block; vertical-align: middle;"/>
@@ -70,12 +64,12 @@ This requires the [moshi package](https://pypi.org/project/moshi/)
 with version 0.2.6 or later, which can be installed via pip.
 
 ```bash
-python -m moshi.run_inference --hf-repo kyutai/stt-2.6b-en bria.mp3
+python -m moshi.run_inference --hf-repo kyutai/stt-2.6b-en audio/bria.mp3
 ```
 
 If you have [uv](https://docs.astral.sh/uv/) installed, you can skip the installation step and run directly:
 ```bash
-uvx --with moshi python -m moshi.run_inference --hf-repo kyutai/stt-2.6b-en bria.mp3
+uvx --with moshi python -m moshi.run_inference --hf-repo kyutai/stt-2.6b-en audio/bria.mp3
 ```
 
 Additionally, we provide two scripts that highlight different usage scenarios. The first script illustrates how to extract word-level timestamps from the model's outputs:
@@ -84,7 +78,7 @@ Additionally, we provide two scripts that highlight different usage scenarios. T
 uv run \
   scripts/transcribe_from_file_via_pytorch.py \
   --hf-repo kyutai/stt-2.6b-en \
-  --file bria.mp3
+  --file audio/bria.mp3
 ```
 
 The second script can be used to run a model on an existing Hugging Face dataset and calculate its performance metrics: 
@@ -130,7 +124,7 @@ uv run scripts/transcribe_from_mic_via_rust_server.py
 
 We also provide a script for transcribing from an audio file.
 ```bash
-uv run scripts/transcribe_from_file_via_rust_server.py bria.mp3
+uv run scripts/transcribe_from_file_via_rust_server.py audio/bria.mp3
 ```
 
 The script limits the decoding speed to simulates real-time processing of the audio. 
@@ -147,7 +141,7 @@ A standalone Rust example script is provided in the `stt-rs` directory in this r
 This can be used as follows:
 ```bash
 cd stt-rs
-cargo run --features cuda -r -- bria.mp3
+cargo run --features cuda -r -- audio/bria.mp3
 ```
 You can get the timestamps by adding the `--timestamps` flag, and see the output
 of the semantic VAD by adding the `--vad` flag.
@@ -164,12 +158,12 @@ This requires the [moshi-mlx package](https://pypi.org/project/moshi-mlx/)
 with version 0.2.6 or later, which can be installed via pip.
 
 ```bash
-python -m moshi_mlx.run_inference --hf-repo kyutai/stt-2.6b-en-mlx bria.mp3 --temp 0
+python -m moshi_mlx.run_inference --hf-repo kyutai/stt-2.6b-en-mlx audio/bria.mp3 --temp 0
 ```
 
 If you have [uv](https://docs.astral.sh/uv/) installed, you can skip the installation step and run directly:
 ```bash
-uvx --with moshi-mlx python -m moshi_mlx.run_inference --hf-repo kyutai/stt-2.6b-en-mlx bria.mp3 --temp 0
+uvx --with moshi-mlx python -m moshi_mlx.run_inference --hf-repo kyutai/stt-2.6b-en-mlx audio/bria.mp3 --temp 0
 ```
 It will install the moshi package in a temporary environment and run the speech-to-text.
 

configs/config-stt-en-hf.toml

@@ -1,7 +1,7 @@
 static_dir = "./static/"
 log_dir = "$HOME/tmp/tts-logs"
 instance_name = "tts"
-authorized_ids = ["open_token"]
+authorized_ids = ["public_token"]
 
 [modules.asr]
 path = "/api/asr-streaming"

configs/config-stt-en_fr-hf.toml

@@ -1,7 +1,7 @@
 static_dir = "./static/"
 log_dir = "$HOME/tmp/tts-logs"
 instance_name = "tts"
-authorized_ids = ["open_token"]
+authorized_ids = ["public_token"]
 
 [modules.asr]
 path = "/api/asr-streaming"

scripts/transcribe_from_file_via_rust_server.py

@@ -9,43 +9,36 @@
 # ///
 import argparse
 import asyncio
-import json
 import time
 
 import msgpack
+import numpy as np
 import sphn
 import websockets
 
-# Desired audio properties
-TARGET_SAMPLE_RATE = 24000
-TARGET_CHANNELS = 1  # Mono
-HEADERS = {"kyutai-api-key": "open_token"}
-all_text = []
-transcript = []
-finished = False
+SAMPLE_RATE = 24000
+FRAME_SIZE = 1920  # Send data in chunks
 
 
 def load_and_process_audio(file_path):
     """Load an MP3 file, resample to 24kHz, convert to mono, and extract PCM float32 data."""
-    pcm_data, _ = sphn.read(file_path, sample_rate=TARGET_SAMPLE_RATE)
+    pcm_data, _ = sphn.read(file_path, sample_rate=SAMPLE_RATE)
     return pcm_data[0]
 
 
 async def receive_messages(websocket):
-    global all_text
-    global transcript
-    global finished
-    try:
+    transcript = []
+
     async for message in websocket:
         data = msgpack.unpackb(message, raw=False)
         if data["type"] == "Step":
+            # This message contains the signal from the semantic VAD, and tells us how
+            # much audio the server has already processed. We don't use either here.
             continue
-            print("received:", data)
         if data["type"] == "Word":
-                all_text.append(data["text"])
+            print(data["text"], end=" ", flush=True)
             transcript.append(
                 {
-                        "speaker": "SPEAKER_00",
                     "text": data["text"],
                     "timestamp": [data["start_time"], data["start_time"]],
                 }
@@ -54,84 +47,89 @@ async def receive_messages(websocket):
             if len(transcript) > 0:
                 transcript[-1]["timestamp"][1] = data["stop_time"]
         if data["type"] == "Marker":
-                print("Received marker, stopping stream.")
+            # Received marker, stopping stream
             break
-    except websockets.ConnectionClosed:
-        print("Connection closed while receiving messages.")
-    finished = True
+
+    return transcript
 
 
 async def send_messages(websocket, rtf: float):
-    global finished
     audio_data = load_and_process_audio(args.in_file)
-    try:
+
+    async def send_audio(audio: np.ndarray):
+        await websocket.send(
+            msgpack.packb(
+                {"type": "Audio", "pcm": [float(x) for x in audio]},
+                use_single_float=True,
+            )
+        )
+
     # Start with a second of silence.
     # This is needed for the 2.6B model for technical reasons.
-        chunk = {"type": "Audio", "pcm": [0.0] * 24000}
-        msg = msgpack.packb(chunk, use_bin_type=True, use_single_float=True)
-        await websocket.send(msg)
+    await send_audio([0.0] * SAMPLE_RATE)
 
-        chunk_size = 1920  # Send data in chunks
     start_time = time.time()
-        for i in range(0, len(audio_data), chunk_size):
-            chunk = {
-                "type": "Audio",
-                "pcm": [float(x) for x in audio_data[i : i + chunk_size]],
-            }
-            msg = msgpack.packb(chunk, use_bin_type=True, use_single_float=True)
-            await websocket.send(msg)
-            expected_send_time = start_time + (i + 1) / 24000 / rtf
+    for i in range(0, len(audio_data), FRAME_SIZE):
+        await send_audio(audio_data[i : i + FRAME_SIZE])
+
+        expected_send_time = start_time + (i + 1) / SAMPLE_RATE / rtf
         current_time = time.time()
         if current_time < expected_send_time:
             await asyncio.sleep(expected_send_time - current_time)
         else:
             await asyncio.sleep(0.001)
-        chunk = {"type": "Audio", "pcm": [0.0] * 1920 * 5}
-        msg = msgpack.packb(chunk, use_bin_type=True, use_single_float=True)
-        await websocket.send(msg)
-        msg = msgpack.packb(
-            {"type": "Marker", "id": 0}, use_bin_type=True, use_single_float=True
+
+    for _ in range(5):
+        await send_audio([0.0] * SAMPLE_RATE)
+
+    # Send a marker to indicate the end of the stream.
+    await websocket.send(
+        msgpack.packb({"type": "Marker", "id": 0}, use_single_float=True)
     )
-        await websocket.send(msg)
+
+    # We'll get back the marker once the corresponding audio has been transcribed,
+    # accounting for the delay of the model. That's why we need to send some silence
+    # after the marker, because the model will not return the marker immediately.
     for _ in range(35):
-            chunk = {"type": "Audio", "pcm": [0.0] * 1920}
-            msg = msgpack.packb(chunk, use_bin_type=True, use_single_float=True)
-            await websocket.send(msg)
-        while True:
-            if finished:
-                break
-            await asyncio.sleep(1.0)
-            # Keep the connection alive as there is a 20s timeout on the rust side.
-            await websocket.ping()
-    except websockets.ConnectionClosed:
-        print("Connection closed while sending messages.")
+        await send_audio([0.0] * SAMPLE_RATE)
 
 
-async def stream_audio(url: str, rtf: float):
+async def stream_audio(url: str, api_key: str, rtf: float):
     """Stream audio data to a WebSocket server."""
+    headers = {"kyutai-api-key": api_key}
 
-    async with websockets.connect(url, additional_headers=HEADERS) as websocket:
+    # Instead of using the header, you can authenticate by adding `?auth_id={api_key}` to the URL
+    async with websockets.connect(url, additional_headers=headers) as websocket:
         send_task = asyncio.create_task(send_messages(websocket, rtf))
         receive_task = asyncio.create_task(receive_messages(websocket))
-        await asyncio.gather(send_task, receive_task)
-    print("exiting")
+        _, transcript = await asyncio.gather(send_task, receive_task)
+
+    return transcript
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("in_file")
-    parser.add_argument("--transcript")
     parser.add_argument(
         "--url",
         help="The url of the server to which to send the audio",
         default="ws://127.0.0.1:8080",
     )
-    parser.add_argument("--rtf", type=float, default=1.01)
+    parser.add_argument("--api-key", default="public_token")
+    parser.add_argument(
+        "--rtf",
+        type=float,
+        default=1.01,
+        help="The real-time factor of how fast to feed in the audio.",
+    )
     args = parser.parse_args()
 
     url = f"{args.url}/api/asr-streaming"
-    asyncio.run(stream_audio(url, args.rtf))
-    print(" ".join(all_text))
-    if args.transcript is not None:
-        with open(args.transcript, "w") as fobj:
-            json.dump({"transcript": transcript}, fobj, indent=4)
+    transcript = asyncio.run(stream_audio(url, args.api_key, args.rtf))
+
+    print()
+    print()
+    for word in transcript:
+        print(
+            f"{word['timestamp'][0]:7.2f} -{word['timestamp'][1]:7.2f}  {word['text']}"
+        )

scripts/transcribe_from_mic_via_rust_server.py

@@ -16,43 +16,59 @@ import numpy as np
 import sounddevice as sd
 import websockets
 
-# Desired audio properties
-TARGET_SAMPLE_RATE = 24000
-TARGET_CHANNELS = 1  # Mono
-audio_queue = asyncio.Queue()
+SAMPLE_RATE = 24000
+
+# The VAD has several prediction heads, each of which tries to determine whether there
+# has been a pause of a given length. The lengths are 0.5, 1.0, 2.0, and 3.0 seconds.
+# Lower indices predict pauses more aggressively. In Unmute, we use 2.0 seconds = index 2.
+PAUSE_PREDICTION_HEAD_INDEX = 2
 
 
-async def receive_messages(websocket):
+async def receive_messages(websocket, show_vad: bool = False):
     """Receive and process messages from the WebSocket server."""
     try:
+        speech_started = False
         async for message in websocket:
             data = msgpack.unpackb(message, raw=False)
-            if data["type"] == "Word":
+
+            # The Step message only gets sent if the model has semantic VAD available
+            if data["type"] == "Step" and show_vad:
+                pause_prediction = data["prs"][PAUSE_PREDICTION_HEAD_INDEX]
+                if pause_prediction > 0.5 and speech_started:
+                    print("| ", end="", flush=True)
+                    speech_started = False
+
+            elif data["type"] == "Word":
                 print(data["text"], end=" ", flush=True)
+                speech_started = True
     except websockets.ConnectionClosed:
         print("Connection closed while receiving messages.")
 
 
-async def send_messages(websocket):
+async def send_messages(websocket, audio_queue):
     """Send audio data from microphone to WebSocket server."""
     try:
         # Start by draining the queue to avoid lags
         while not audio_queue.empty():
             await audio_queue.get()
+
         print("Starting the transcription")
+
         while True:
             audio_data = await audio_queue.get()
             chunk = {"type": "Audio", "pcm": [float(x) for x in audio_data]}
             msg = msgpack.packb(chunk, use_bin_type=True, use_single_float=True)
             await websocket.send(msg)
+
     except websockets.ConnectionClosed:
         print("Connection closed while sending messages.")
 
 
-async def stream_audio(url: str, api_key: str):
+async def stream_audio(url: str, api_key: str, show_vad: bool):
     """Stream audio data to a WebSocket server."""
     print("Starting microphone recording...")
     print("Press Ctrl+C to stop recording")
+    audio_queue = asyncio.Queue()
 
     loop = asyncio.get_event_loop()
 
@@ -63,16 +79,19 @@ async def stream_audio(url: str, api_key: str):
 
     # Start audio stream
     with sd.InputStream(
-        samplerate=TARGET_SAMPLE_RATE,
-        channels=TARGET_CHANNELS,
+        samplerate=SAMPLE_RATE,
+        channels=1,
         dtype="float32",
         callback=audio_callback,
         blocksize=1920,  # 80ms blocks
     ):
         headers = {"kyutai-api-key": api_key}
+        # Instead of using the header, you can authenticate by adding `?auth_id={api_key}` to the URL
         async with websockets.connect(url, additional_headers=headers) as websocket:
-            send_task = asyncio.create_task(send_messages(websocket))
-            receive_task = asyncio.create_task(receive_messages(websocket))
+            send_task = asyncio.create_task(send_messages(websocket, audio_queue))
+            receive_task = asyncio.create_task(
+                receive_messages(websocket, show_vad=show_vad)
+            )
             await asyncio.gather(send_task, receive_task)
 
 
@@ -83,18 +102,23 @@ if __name__ == "__main__":
         help="The URL of the server to which to send the audio",
         default="ws://127.0.0.1:8080",
     )
-    parser.add_argument("--api-key", default="open_token")
+    parser.add_argument("--api-key", default="public_token")
     parser.add_argument(
         "--list-devices", action="store_true", help="List available audio devices"
     )
     parser.add_argument(
         "--device", type=int, help="Input device ID (use --list-devices to see options)"
     )
+    parser.add_argument(
+        "--show-vad",
+        action="store_true",
+        help="Visualize the predictions of the semantic voice activity detector with a '|' symbol",
+    )
 
     args = parser.parse_args()
 
     def handle_sigint(signum, frame):
-        print("Interrupted by user")
+        print("Interrupted by user")  # Don't complain about KeyboardInterrupt
         exit(0)
 
     signal.signal(signal.SIGINT, handle_sigint)
@@ -108,4 +132,4 @@ if __name__ == "__main__":
         sd.default.device[0] = args.device  # Set input device
 
     url = f"{args.url}/api/asr-streaming"
-    asyncio.run(stream_audio(url, args.api_key))
+    asyncio.run(stream_audio(url, args.api_key, args.show_vad))