Commit f5834d3
Changed files (6)
src/asr/groq.py
@@ -0,0 +1,313 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import io
+import re
+import tempfile
+from decimal import Decimal
+from pathlib import Path
+
+import anyio
+from glom import glom
+from loguru import logger
+from pydub import AudioSegment
+
+from asr.utils import downsampe_audio
+from config import ASR
+from networking import hx_req
+from utils import seconds_to_time, strings_list
+
+
+async def groq_asr(path: str | Path, model: str = "", prompt: str = "", temperature: float = 0, language: str = "") -> dict:
+ """Groq ASR.
+
+ https://console.groq.com/docs/api-reference#audio-transcription
+
+ Returns:
+ {"texts": str, "raw_texts": str, "segments": list[dict], "error": str}
+ """
+ path = Path(path).expanduser().resolve()
+ if not path.is_file():
+ return {"texts": "", "error": "File not found."}
+ audio_path = path if path.suffix.lower() not in [".mp3", ".wav", ".ogg"] else await downsampe_audio(path, ext="ogg", codec="libopus")
+ # max allowed file size is 25MB
+ if audio_path.stat().st_size < ASR.GROQ_MAX_BYTES:
+ return await groq_single_file(audio_path, model=model, prompt=prompt, temperature=temperature, language=language)
+ return await groq_file_chunks(audio_path, model=model, temperature=temperature, prompt=prompt, language=language)
+
+
+async def groq_single_file(
+ path: Path | str,
+ model: str = "",
+ temperature: float = 0,
+ prompt: str = "",
+ language: str = "",
+ start_seconds: float = 0,
+) -> dict:
+ """Transcribe a single audio chunk with Groq API.
+
+ Returns:
+ {"texts": str, "raw_texts": str, "segments": list[dict], "error": str}
+ """
+ path = Path(path).expanduser().resolve()
+ if not path.is_file():
+ return {"texts": "", "error": "File not found."}
+ if not model:
+ model = strings_list(ASR.GROQ_MODELS, shuffle=True)[0]
+ async with await anyio.open_file(path, "rb") as f:
+ content = await f.read()
+ content_bytes = io.BytesIO(content)
+ data = {
+ "model": model,
+ "temperature": str(temperature), # must be string
+ "response_format": "verbose_json",
+ }
+ if prompt:
+ data["prompt"] = prompt
+ if language:
+ data["language"] = language
+ resp = await hx_req(
+ "https://api.groq.com/openai/v1/audio/transcriptions",
+ method="POST",
+ headers={"Authorization": f"Bearer {strings_list(ASR.GROQ_KEYS, shuffle=True)[0]}"},
+ files={"file": ("chunk.ogg", content_bytes, "audio/ogg")},
+ data=data,
+ timeout=600,
+ proxy=ASR.GROQ_PROXY,
+ check_kv={"task": "transcribe"},
+ check_keys=["segments"],
+ )
+ start = Decimal(start_seconds).quantize(Decimal(".01"))
+ resp["segments"] = [
+ {
+ "start": start + Decimal(str(seg["start"])),
+ "end": start + Decimal(str(seg["end"])),
+ "text": seg["text"],
+ }
+ for seg in resp.get("segments", [])
+ ]
+ resp["raw_texts"] = " ".join(x["text"] for x in resp["segments"])
+ resp["texts"] = "\n".join(f"[{seconds_to_time(x['start'])}] {x['text'].lstrip()}" for x in resp["segments"]) # with timestamp
+ if resp.get("hx_error"):
+ resp["error"] = resp.pop("hx_error")
+ return resp
+
+
+def find_longest_common_sequence(text_seq: list[str], *, match_by_words: bool = False) -> str:
+ """Find the optimal alignment between sequences with longest common sequence and sliding window matching.
+
+ Note: `match_by_words` works great for English sequences, but not so much for Chinese.
+
+ Args:
+ text_seq: List of text sequences to align and merge
+ match_by_words: Whether to match by words (True) or characters (False)
+
+ Returns:
+ str: Merged sequence with optimal alignment
+
+ Raises:
+ RuntimeError: If there's a mismatch in sequence lengths during comparison
+ """
+ if not text_seq:
+ return ""
+
+ # Convert input based on matching 1strategy
+ sequences = [[word for word in re.split(r"(\s+\w+)", seq) if word] for seq in text_seq] if match_by_words else [list(seq) for seq in text_seq]
+
+ left_sequence = sequences[0]
+ left_length = len(left_sequence)
+ total_sequence = []
+ for right_sequence in sequences[1:]:
+ max_matching = 0.0
+ right_length = len(right_sequence)
+ max_indices = (left_length, left_length, 0, 0) # left_start, left_stop, right_start, right_stop
+
+ # Try different alignments
+ for i in range(1, left_length + right_length + 1):
+ # Add epsilon to favor longer matches
+ eps = float(i) / 10000.0
+
+ left_start = max(0, left_length - i)
+ left_stop = min(left_length, left_length + right_length - i)
+ left = left_sequence[left_start:left_stop]
+
+ right_start = max(0, i - left_length)
+ right_stop = min(right_length, i)
+ right = right_sequence[right_start:right_stop]
+
+ if len(left) != len(right):
+ msg = "Mismatched subsequences detected during transcript merging."
+ raise RuntimeError(msg)
+
+ matches = sum(a == b for a, b in zip(left, right, strict=False))
+
+ # Normalize matches by position and add epsilon
+ matching = matches / float(i) + eps
+
+ # Require at least 2 matches
+ if matches > 1 and matching > max_matching:
+ max_matching = matching
+ max_indices = (left_start, left_stop, right_start, right_stop)
+
+ # Use the best alignment found
+ left_start, left_stop, right_start, right_stop = max_indices
+ # Take left half from left sequence and right half from right sequence
+ left_mid = (left_stop + left_start) // 2
+ right_mid = (right_stop + right_start) // 2
+
+ total_sequence.extend(left_sequence[:left_mid])
+ left_sequence = right_sequence[right_mid:]
+ left_length = len(left_sequence)
+
+ # Add remaining sequence
+ total_sequence.extend(left_sequence)
+
+ # Join back into text
+ return "".join(total_sequence)
+
+
+def merge_transcripts(results: list[dict]) -> dict:
+ """Merge transcription chunks and handle overlaps.
+
+ Args:
+ results: List of segments
+
+ Returns:
+ {"texts": str, "raw_texts": str, "segments": list[dict]}
+ """
+ processed_segments = []
+ processed_chunks = []
+ for i, chunk in enumerate(results):
+ segments = chunk.get("segments") or []
+ # If not last chunk, find next chunk start time
+ if i < len(results) - 1:
+ next_start = glom(results[i + 1], "segments.0.start", default=float("inf")) # This is in milliseconds
+
+ # Split segments into current and overlap based on next chunk's start time
+ current_segments = []
+ overlap_segments = []
+
+ for segment in segments:
+ # Convert segment end time to ms and compare with next chunk start time
+ if segment["end"] > next_start:
+ overlap_segments.append(segment)
+ else:
+ current_segments.append(segment)
+
+ # Merge overlap segments if any exist
+ if overlap_segments:
+ merged_overlap = overlap_segments[0].copy()
+ merged_overlap |= {
+ "text": " ".join(s.get("text", "") for s in overlap_segments),
+ "end": overlap_segments[-1].get("end", 0),
+ }
+
+ current_segments.append(merged_overlap)
+
+ processed_chunks.append(current_segments)
+ else: # last chunk
+ processed_chunks.append(segments)
+ # Merge boundaries between chunks
+ for i in range(len(processed_chunks) - 1):
+ # Skip if either chunk has no segments
+ if not processed_chunks[i] or not processed_chunks[i + 1]:
+ continue
+
+ # Add all segments except last from current chunk
+ if len(processed_chunks[i]) > 1:
+ processed_segments.extend(processed_chunks[i][:-1])
+
+ # Merge boundary segments
+ last_segment = processed_chunks[i][-1]
+ first_segment = processed_chunks[i + 1][0]
+
+ merged_text = find_longest_common_sequence([last_segment.get("text", ""), first_segment.get("text", "")])
+ merged_segment = last_segment.copy()
+
+ merged_segment |= {"text": merged_text, "end": first_segment.get("end", 0)}
+ processed_segments.append(merged_segment)
+
+ # Add all segments from last chunk
+ if processed_chunks and processed_chunks[-1]:
+ processed_segments.extend(processed_chunks[-1])
+
+ # Filter out duplicated segments
+ final_segments = []
+ for idx, seg in enumerate(processed_segments):
+ if idx == 0: # the first segment is always added
+ final_segments.append(seg)
+ continue
+ if seg["start"] >= processed_segments[idx - 1]["end"]: # ensure no overlap
+ final_segments.append(seg)
+ # Create final transcription
+ raw_texts = ""
+ texts = "" # with timestamp
+ for x in final_segments:
+ raw_texts += x["text"].lstrip() + " "
+ texts += f"[{seconds_to_time(x['start'])}] {x['text'].lstrip()}\n"
+ return {"texts": texts.strip(), "raw_texts": raw_texts.strip(), "segments": final_segments}
+
+
+async def groq_file_chunks(
+ path: Path,
+ chunk_seconds: float = ASR.GROQ_CHUNK_SECONDS,
+ overlap_seconds: float = ASR.GROQ_OVERLAP_SECONDS,
+ model: str = "",
+ temperature: float = 0,
+ prompt: str = "",
+ language: str = "",
+) -> dict:
+ """Transcribe audio in chunks with overlap.
+
+ Args:
+ path: Path to audio file
+ chunk_seconds: Length of each chunk in seconds
+ overlap_seconds: Overlap between chunks in seconds
+
+ Returns:
+ dict: {"texts": str, "raw_texts": str, "segments": list[dict]}
+
+ Raises:
+ RuntimeError: If audio file fails to load
+ """
+ path = Path(path).expanduser().resolve()
+ if not path.is_file():
+ return {"texts": "", "error": "File not found."}
+ if path.suffix.lower() not in [".opus", ".ogg"]:
+ path = await downsampe_audio(path, ext="ogg", codec="libopus")
+ try:
+ audio = AudioSegment.from_ogg(path)
+ except Exception as e:
+ msg = f"Failed to load audio: {e!s}"
+ raise RuntimeError(msg) from e
+
+ transcription = {}
+ try:
+ # Calculate # of chunks
+ duration = len(audio)
+ chunk_ms = round(chunk_seconds * 1000)
+ overlap_ms = round(overlap_seconds * 1000)
+ total_chunks = (duration // (chunk_ms - overlap_ms)) + 1
+ results = []
+
+ # Loop through each chunk, extract current chunk from audio, transcribe
+ for i in range(total_chunks):
+ start = i * (chunk_ms - overlap_ms)
+ end = min(start + chunk_ms, duration)
+ logger.trace(f"\nProcessing chunk {i + 1}/{total_chunks}, Time range: {start / 1000:.1f}s - {end / 1000:.1f}s")
+ chunk = audio[start:end]
+ with tempfile.NamedTemporaryFile(suffix=".ogg") as chunk_file:
+ chunk.export(chunk_file.name, format="ogg")
+ result = await groq_single_file(
+ chunk_file.name,
+ start_seconds=start / 1000,
+ model=model,
+ temperature=temperature,
+ prompt=prompt,
+ language=language,
+ )
+ results.append(result)
+ transcription = merge_transcripts(results)
+ except Exception as e:
+ logger.error(e)
+ return {"error": str(e)}
+ return transcription
src/asr/utils.py
@@ -32,8 +32,8 @@ def get_asr_method(duration: float, file_size: int, force_engine: str = "") -> t
return get_tencent_asr_method(duration, file_size)
if force_engine == "gemini":
return get_gemini_asr_method(duration)
- if force_engine == "cloudflare":
- return "cloudflare", ["mp3", "opus"]
+ if force_engine in ["cloudflare", "groq"]:
+ return force_engine.lower(), ["mp3", "opus", "ogg", "wav", "flac", "aac"]
if asr_engine == "ali":
return get_ali_asr_method()
@@ -43,8 +43,8 @@ def get_asr_method(duration: float, file_size: int, force_engine: str = "") -> t
return get_tencent_asr_method(duration, file_size)
if asr_engine.lower() == "gemini":
return get_gemini_asr_method(duration)
- if asr_engine.lower() == "cloudflare":
- return "cloudflare", ["mp3", "opus"]
+ if asr_engine.lower() in ["cloudflare", "groq"]:
+ return asr_engine.lower(), ["mp3", "opus", "ogg", "wav", "flac", "aac"]
return f"ASR Engine: {asr_engine} is not support for duration: {duration}, filesize: {file_size}", []
src/asr/voice_recognition.py
@@ -14,6 +14,7 @@ from asr.ali_asr import ali_asr
from asr.cloudflare import cloudflare_asr
from asr.deepgram import deepgram_asr
from asr.gemini_asr import gemini_stream_asr
+from asr.groq import groq_asr
from asr.tecent_asr import tencent_async_asr, tencent_flash_asr, tencent_single_asr
from asr.utils import get_asr_method
from config import CAPTION_LENGTH, PREFIX, TEXT_LENGTH
@@ -122,7 +123,7 @@ async def voice_to_text(
custom_code = custom_code.replace("fy", "zh_dialect")
if f"16k_{custom_code}" in ENGINE_MAP:
asr_language = f"16k_{custom_code}"
- elif custom_code in ["gemini", "tencent", "ali", "deepgram", "cloudflare"]:
+ elif custom_code in ["gemini", "tencent", "ali", "deepgram", "cloudflare", "groq"]:
force_engine = custom_code
msg = f"[ASR] 收到消息: {trigger_info['mtype']}, 开始识别..."
@@ -194,7 +195,7 @@ async def asr_file(
if duration == 0:
duration = info["duration"]
asr_method, supported_ext = get_asr_method(duration, file_size=Path(path).stat().st_size, force_engine=engine)
- if asr_method not in ["ali", "deepgram", "cloudflare", "tencent_single_asr", "tencent_flash_asr", "tencent_async_asr", "gemini"]:
+ if asr_method not in ["ali", "deepgram", "cloudflare", "tencent_single_asr", "tencent_flash_asr", "tencent_async_asr", "gemini", "groq"]:
return {"error": asr_method}
voice_format = path.suffix.lstrip(".")
@@ -231,6 +232,8 @@ async def asr_file(
res = await gemini_stream_asr(path=path, voice_format=voice_format, delete_gemini_file=delete_gemini_file, **kwargs)
elif asr_method == "cloudflare":
res = await cloudflare_asr(path=path, model=kwargs.get("cf_asr_model", ""), prompt=kwargs.get("cf_asr_prompt", ""))
+ elif asr_method == "groq":
+ res = await groq_asr(path=path, model=kwargs.get("groq_asr_model", ""), prompt=kwargs.get("groq_asr_prompt", ""))
else:
return {"error": "ASR method not supported"}
if res.get("texts"):
src/config.py
@@ -258,6 +258,12 @@ class ASR:
DEEPGRAM_API = os.getenv("ASR_DEEPGRAM_API", "") # comma separated keys for load balance. e.g. "key1,key2,key3"
CLOUDFLARE_MODEL = os.getenv("ASR_CLOUDFLARE_MODEL", "@cf/openai/whisper-large-v3-turbo")
CLOUDFLARE_KEYS = os.getenv("ASR_CLOUDFLARE_KEYS", "") # comma separated keys for load balance. e.g. "AccountID:API_TOKEN, AccountID:API_TOKEN, ..."
+ GROQ_PROXY = os.getenv("ASR_GROQ_PROXY", None) # Ban CN & HK IP
+ GROQ_MAX_BYTES = int(os.getenv("ASR_GROQ_MAX_BYTES", "26214400")) # 25MB (max file bytes for single file)
+ GROQ_CHUNK_SECONDS = float(os.getenv("ASR_GROQ_CHUNK_SECONDS", "1800")) # split long audio file into chunks
+ GROQ_OVERLAP_SECONDS = float(os.getenv("ASR_GROQ_OVERLAP_SECONDS", "10")) # overlap seconds between chunks
+ GROQ_KEYS = os.getenv("ASR_GROQ_KEYS", "") # comma separated keys for load balance.
+ GROQ_MODELS = os.getenv("ASR_GROQ_MODELS", "whisper-large-v3") # comma separated model names.
class PODCAST:
pyproject.toml
@@ -21,6 +21,7 @@ dependencies = [
"pillow-heif==0.22.0",
"pillow==11.2.1",
"puremagic==1.29",
+ "pydub==0.25.1",
"pyrotgfork==2.2.12",
"pysocks==1.7.1",
"pytgcrypto==1.2.11",
uv.lock
@@ -242,6 +242,7 @@ dependencies = [
{ name = "pillow" },
{ name = "pillow-heif" },
{ name = "puremagic" },
+ { name = "pydub" },
{ name = "pyrotgfork" },
{ name = "pysocks" },
{ name = "pytgcrypto" },
@@ -286,6 +287,7 @@ requires-dist = [
{ name = "pillow", specifier = "==11.2.1" },
{ name = "pillow-heif", specifier = "==0.22.0" },
{ name = "puremagic", specifier = "==1.29" },
+ { name = "pydub", specifier = "==0.25.1" },
{ name = "pyrotgfork", specifier = "==2.2.12" },
{ name = "pysocks", specifier = "==1.7.1" },
{ name = "pytgcrypto", specifier = "==1.2.11" },
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