使用OpenAI API比较语音转文字方法

!pip install --upgrade -q openai openai-agents websockets sounddevice pyaudio nest_asyncio resampy httpx websocket-client

TARGET_SR     = 24_000
PCM_SCALE     = 32_767
CHUNK_SAMPLES = 3_072                 # ≈128 ms at 24 kHz
RT_URL        = "wss://api.openai.com/v1/realtime?intent=transcription"

EV_DELTA      = "conversation.item.input_audio_transcription.delta"
EV_DONE       = "conversation.item.input_audio_transcription.completed"
# ── helpers ────────────────────────────────────────────────────────────────
def float_to_16bit_pcm(float32_array):
    clipped = [max(-1.0, min(1.0, x)) for x in float32_array]
    pcm16 = b''.join(struct.pack('<h', int(x * 32767)) for x in clipped)
    return pcm16

def base64_encode_audio(float32_array):
    pcm_bytes = float_to_16bit_pcm(float32_array)
    encoded = base64.b64encode(pcm_bytes).decode('ascii')
    return encoded

def load_and_resample(path: str, sr: int = TARGET_SR) -> np.ndarray:
    """Return mono PCM-16 as a NumPy array."""
    data, file_sr = sf.read(path, dtype="float32")
    if data.ndim > 1:
        data = data.mean(axis=1)
    if file_sr != sr:
        data = resampy.resample(data, file_sr, sr)
    return data

async def _send_audio(ws, pcm: np.ndarray, chunk: int, sr: int) -> None:
    """Producer: stream base-64 chunks at real-time pace, then signal EOF."""
    dur = 0.025 # Add pacing to ensure real-time transcription
    t_next = time.monotonic()

    for i in range(0, len(pcm), chunk):
        float_chunk = pcm[i:i + chunk]
        payload = {
            "type":  "input_audio_buffer.append",
            "audio": base64_encode_audio(float_chunk),
        }
        await ws.send(json.dumps(payload))
        t_next += dur
        await asyncio.sleep(max(0, t_next - time.monotonic()))

    await ws.send(json.dumps({"type": "input_audio_buffer.end"}))

async def _recv_transcripts(ws, collected: List[str]) -> None:
    """
    Consumer: build `current` from streaming deltas, promote it to `collected`
    whenever a …completed event arrives, and flush the remainder on socket
    close so no words are lost.
    """
    current: List[str] = []

    try:
        async for msg in ws:
            ev = json.loads(msg)

            typ = ev.get("type")
            if typ == EV_DELTA:
                delta = ev.get("delta")
                if delta:
                    current.append(delta)
                    print(delta, end="", flush=True)
            elif typ == EV_DONE:
                # sentence finished → move to permanent list
                collected.append("".join(current))
                current.clear()
    except websockets.ConnectionClosedOK:
        pass

    # socket closed → flush any remaining partial sentence
    if current:
        collected.append("".join(current))

def _session(model: str, vad: float = 0.5) -> dict:
    return {
        "type": "transcription_session.update",
        "session": {
            "input_audio_format": "pcm16",
            "turn_detection": {"type": "server_vad", "threshold": vad},
            "input_audio_transcription": {"model": model},
        },
    }

async def transcribe_audio_async(
    wav_path,
    api_key,
    *,
    model: str = MODEL_NAME,
    chunk: int = CHUNK_SAMPLES,
) -> str:
    pcm = load_and_resample(wav_path)
    headers = {"Authorization": f"Bearer {api_key}", "OpenAI-Beta": "realtime=v1"}

    async with websockets.connect(RT_URL, additional_headers=headers, max_size=None) as ws:
        await ws.send(json.dumps(_session(model)))

        transcripts: List[str] = []
        await asyncio.gather(
            _send_audio(ws, pcm, chunk, TARGET_SR),
            _recv_transcripts(ws, transcripts),
        )  # returns when server closes

    return " ".join(transcripts)

# ── 1 · agent that replies in French ---------------------------------------
fr_agent = Agent(
    name="Assistant-FR",
    instructions=
        "Translate the user's words into French.",
    model="gpt-4o-mini",
)

# ── 2 · workflow that PRINTS what it yields --------------------------------
class PrintingWorkflow(SingleAgentVoiceWorkflow):
    """Subclass that prints every chunk it yields (the agent's reply)."""

    async def run(self, transcription: str):
        # Optionally: also print the user transcription
        print()
        print("[User]:", transcription)
        print("[Assistant]: ", end="", flush=True)
        async for chunk in super().run(transcription):
            print(chunk, end="", flush=True)   # <-- agent (French) text
            yield chunk                        # still forward to TTS


pipeline = VoicePipeline(
    workflow=PrintingWorkflow(fr_agent),
    stt_model=MODEL_NAME,
    config=VoicePipelineConfig(tracing_disabled=True),
)

# ── 3 · helper to stream ~40 ms chunks at 24 kHz ---------------------------
def load_and_resample(path: str, sr: int = 24_000) -> np.ndarray:
    """Return mono PCM-16 as a NumPy array."""
    data, file_sr = sf.read(path, dtype="float32")
    if data.ndim > 1:
        data = data.mean(axis=1)
    if file_sr != sr:
        data = resampy.resample(data, file_sr, sr)
    return data
        
def audio_chunks(path: str, target_sr: int = 24_000, chunk_ms: int = 40):
    # 1️⃣ reuse the helper
    audio = load_and_resample(path, target_sr)

    # 2️⃣ float-32 → int16 NumPy array
    pcm = (np.clip(audio, -1, 1) * 32_767).astype(np.int16)

    # 3️⃣ yield real-time sized hops
    hop = int(target_sr * chunk_ms / 1_000)
    for off in range(0, len(pcm), hop):
        yield pcm[off : off + hop]

# ── 4 · stream the file ----------------------------------------------------
async def stream_audio(path: str):
    sai = StreamedAudioInput()
    run_task = asyncio.create_task(pipeline.run(sai))

    for chunk in audio_chunks(path):
        await sai.add_audio(chunk)
        await asyncio.sleep(len(chunk) / 24_000)   # real-time pacing

    # just stop pushing; session ends automatically
    await run_task        # wait for pipeline to finish